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Hey builders. I've been working on a SaaS called Profiteer.

The core idea is simple: most e-com stores don't fail because of the product, they fail because their marketing angle is generic, and they use the exact same ad copy as 100 other competitors.

I just launched the beta version of our first tool, 'FindYourAngle'. You paste an AliExpress or Amazon URL, and the AI (Claude 3.5 under the hood) generates 5 specific marketing angles, emotional pain points, and ready-to-use ad scripts for TikTok, IG, and FB.

The stack is React + Tailwind + Python + Supabase.

We are currently moving away from our dark "generic AI" theme to a much cleaner, lighter UI, but I want to make sure the core engine actually provides value before spending weeks on the redesign.

I need some brutal honesty:

1. Does the AI actually generate good, usable angles for the products you test, or is it too generic?
2. Is the UI/UX intuitive enough for a first-time user?
3. What is the biggest missing feature that would stop you from paying for this?

Right now it's completely free to try (every new account gets 5 free analyses, no credit card required).

You can break it here: https://find-your-angle.vercel.app/

Please roast the concept, the UI, the output, don't hold back. Thanks!

“I kept losing all my context every time I switched between AI tools and got frustrated enough to build something. Context Vault lets you paste any AI conversation and get a formatted memory pack optimized for whichever AI you want to use next. No more re-explaining yourself from scratch. It’s free right now — www.contextvault.cloud — would genuinely love to know if this solves a real problem for anyone else or where it falls short.”

I noticed something that even when I’m with friends, I still end up doomscrolling longer than I should.

So I’ve developed an app that helps users limit excessive phone usage through gentle reminders, with a focus on staying present in real-life situations (e.g. studying, working, and etc.).

It supports both individual use and local group sessions.

The app is still early, and I’m currently looking for honest feedback from users. All features are free, and I just want to help others struggling with too much phone too.

If you’re interested:
https://play.google.com/store/apps/details?id=com.unfib

I'm still a noob at using comfyui, but I always wanted to do more. I saw a rtx 6000 pro at microcenter and bought it. What would be a good starting software for a noob on Windows 11 for lora training and how big can I go on training data with a single 6000 pro?

The new AI effects in Filmora 15.3 are fun to play with. Some of them look really good and can add a unique style quickly. The only thing I’m cautious about is overusing them, because it’s easy for everything to start looking a bit too similar or “effect-heavy.” Still, when used sparingly, they can really elevate a video without much effort.

How can one guarantee safety for text to image inference?

Consider a mobile app that using text to image can be used in unintentional ways either by a user explicitly creating a prompt that will produce harmful or PG+ rated images, or unintentionally by definition of how a user asks for an innocent image.

I'm creating an app intended for all ages, and it would not be appropriate if a user manages to generate unsafe images.

Ive read about negative prompt and how they could play a role in trying to avoid unsafe content, however, you cannot exclude everything.

Is there any text encoder or any other methods that is tried and tested that I can use which will guarantee safe content only?

Post image generation is also a huge barrier as that will require a second inference on the edge which makes the app unusable thereafter...

Hi, I'm looking for a workflow for object removal from a video. LTX model is now quite popular but I can't find any workflow for v2v object removal. It looks like this type of usage is not so popular as for now VACE 2.1 still seems to perform best but it also often fails to remove masked area and instead just slightly change its shape (no reference image). Is there someone who follow this object removal area and could suggest some good models and workflows for that purpose?

Been thinking a lot about a problem that doesn't get nearly enough attention in the local LLM space: catastrophic forgetting.

You fine-tune on your domain data (medical, legal, code, etc.) and it gets great at that task… but silently loses capability on everything else. The more specialized you make it, the dumber it gets everywhere. Anyone who’s done sequential fine-tuning has seen this firsthand.

It’s a fundamental limitation of how neural networks learn today — new gradients just overwrite old ones. There’s no real separation between fast learning and long-term memory consolidation.

The usual workarounds feel like duct tape:

• LoRA adapters help with efficiency but don’t truly solve forgetting
• Replay buffers are expensive and don’t scale well
• MoE is powerful but not something you can easily add later

We’ve been experimenting with a different approach: a dual-memory architecture loosely inspired by how biological brains separate fast episodic learning from slower semantic consolidation.

Here are some early results from a 5-test suite (learned encoder):

Still very early days and there’s a lot left to validate and scale, but the direction feels fundamentally better than fighting forgetting with more hacks.

Curious what this community thinks:

• Has anyone found actually effective solutions for continual/sequential learning with local models?
• How bad is the forgetting issue for you when doing multi-domain or iterative fine-tuning?
• Do most people just retrain from scratch or keep separate LoRAs per task?

Would love to hear what approaches you’ve tried (or given up on).

Sometimes they show the effort level, sometimes it goes away. Claude team, do you guys like to play this nonsense game, a lot?

I use the Pro model to help me untangle the logic behind complex projects and plans.

Some people claim that version 5.2 is significantly larger in scale than 5.4. While there is constant discussion regarding how 5.4—specifically within Codex—boasts superior capabilities and faster speeds,

I wonder: if we are considering the full-scale large language model accessed via the web interface, would 5.2 actually outperform 5.4?

I don't have a Mac. My only computer is a Windows desktop I originally bought for Overwatch.

Turns out that's a problem when you use Claude Code heavily — because every decent usage tracker, tray monitor, and rate limit widget out there? Mac only.

So I built one.

**WhereMyTokens** — a Windows system tray app that keeps an eye on your Claude Code

usage so you can stay in flow.

**What it shows:**

- Active sessions: token count, cost, status (active / waiting / idle / compacting)

- Rate limit progress bars — 5h and 1w windows, with countdown to reset

- Context window % per session with color warnings (amber → orange → red)

- Tool usage breakdown per session (Read, Edit, Bash…)

- Where Claude *actually* spent your tokens: Thinking, Response, Git, Build, Search, etc.

- Coding productivity via git: commits, net lines changed, Claude ROI ($/1K lines added)

**Privacy:** reads your local `~/.claude/projects/*.jsonl` directly — nothing sent anywhere.

Can also register as a Claude Code `statusLine` plugin for live data without polling.

Since I use this every day, updates have been fast. It's been out ~2 weeks and already on v1.7. Happy to keep improving it based on what Windows Claude Code users actually need.

→ GitHub (MIT, free): https://github.com/jeongwookie/WhereMyTokens

If you're on Windows and use Claude Code, try it and let me know what's missing.

# THE THEORETICAL EXPLORER'S FIELD GUIDE

### A Universal Thinking Tool for Humans and AI

---

## THE CORE TRUTH

```

Known + Unknown = Discovery

Opposite + Same = Complete Picture

Your Hands + AI Brain = Original Territory

Constraint + Curiosity = Invention

Failure + Documentation = Permanent Value

The Unthought + The Overthought = Everything New

```

---

## THE SIX DIRECTIONS OF THINKING

```

FORWARD What comes next

BACKWARD What came before

OPPOSITE What is the reverse

SIDEWAYS What connects from another field

INWARD What is hidden inside this

OUTWARD What does this affect beyond itself

```

---

## THE MASTER QUESTIONS

```

WHAT EXISTS

Map everything known

WHAT DOES NOT EXIST

Find every gap

WHAT HAS NEVER BEEN COMBINED

Smash unrelated things together

WHAT IS THE OPPOSITE

Reverse every assumption

WHAT WOULD HAPPEN IF

Remove one rule at a time

WHAT ALMOST EXISTED

Find abandoned ideas

Find failed experiments

Find stopped research

These are goldmines

WHAT EXISTS IN NATURE

That has never been

replicated by humans

WHAT HAS BEEN ASSUMED

That has never been tested

WHAT WOULD A CHILD ASK

That experts stopped asking

WHAT DOES THIS REMIND YOU OF

In a completely unrelated field

WHAT HAPPENS AT THE EDGES

Where two fields meet

Where two materials meet

Where two ideas meet

That intersection is

almost always unexplored

```

---

## HOW TO USE AI PROPERLY

```

STEP ONE

Dump everything you have

Materials tools constraints

Budget time location

Every limitation you have

More detail equals better results

STEP TWO

Ask for the complete map

Everything known about this

Every combination documented

Every result recorded

Get fully oriented first

STEP THREE

Ask where the map ends

What is undocumented

What is theoretical only

What has been attempted

but never finished

What was abandoned and why

STEP FOUR

Ask for all six directions

Forward backward opposite

Sideways inward outward

Each direction reveals

something the others miss

STEP FIVE

Pick the unexplored edge

Not the safe center

The weird uncomfortable thing

That makes you think

nobody has done this

STEP SIX

Constrain it to what you have

Your limits are your power

Mason jars and wire

become the laboratory

Grocery store becomes

the supply chain

Constraints force creativity

Every single time

STEP SEVEN

Ask what could go wrong

Map every failure mode

Then ask what those

failures might accidentally produce

Aim for interesting failures

Not just success

STEP EIGHT

Ask what you have not thought of

Literally ask AI

What am I missing

What question have I

not asked yet

What direction have I

not looked yet

This step is critical

Most people skip it

STEP NINE

Document everything completely

Every step every result

Every color every smell

Every surprise every failure

Even if it seems

completely unimportant

Document it anyway

STEP TEN

Share it somewhere

Anywhere at all

One post one photo

One video one note

Puts your work

permanently into the world

Others build on it

AI learns from it

You become the source

```

---

## THE COMPLETE CYCLE

```

HAVE AN IDEA

↓

DUMP IT INTO AI

↓

MAP WHAT EXISTS

↓

FIND WHAT DOES NOT

↓

LOOK IN ALL SIX DIRECTIONS

↓

ASK WHAT YOU MISSED

↓

CONSTRAIN TO REALITY

↓

EXPERIMENT BOLDLY

↓

DOCUMENT EVERYTHING

↓

ESPECIALLY THE FAILURES

↓

SHARE SOMEWHERE

↓

FEED RESULTS BACK IN

↓

LOOP BACK

↓

NEVER TRULY ENDS

```

---

## PROBLEMS ARE PORTALS

```

When something fails Stop immediately

When color changes wrong Stop immediately

When result surprises you Stop immediately

When nothing happens Stop immediately

Then ask these questions

What is this actually

What caused this specifically

Has anyone seen this before

What would happen if

I did this on purpose

Could this failure be

more valuable than

what I was attempting

Penicillin was contamination

Teflon was escaped gas

Velcro was a walk in the woods

Post It notes were weak glue

Microwave was a melted chocolate bar

Your failed experiment

is someone else's

most important discovery

Document it completely

```

---

## THE UNTHOUGHT TERRITORY

```

These are the spaces

AI has not mapped yet

Because nobody has gone there

WHERE FIELDS COLLIDE

Chemistry meets cooking

Biology meets architecture

Music meets materials science

Medicine meets geology

Art meets engineering

These collisions produce

the most original territory

WHERE SCALES CHANGE

What works at large scale

might fail at small scale

What fails at large scale

might work at tiny scale

Change the scale of

anything you are doing

Completely different results often

WHERE TIME CHANGES

What happens over seconds

versus years versus centuries

Most experiments stop too early

Most observations are too short

Let things run longer

than seems necessary

WHERE LOCATION CHANGES

What grows in desert

might not grow at coast

What works at sea level

might not work at altitude

Location and environment

change everything subtly

WHERE INTENTION DISAPPEARS

The most interesting results

happen when you stop

trying to get

a specific result

And just observe

what actually wants to happen

Let the materials decide

Then document what they chose

WHERE COMBINATION BECOMES THREE

Most experiments use two things

Adding a third element

creates exponentially more complexity

Most three way combinations

have never been attempted

Your four jar system

with copper zinc iron phosphate

is already in this territory

```

---

## THE SHARING MAP

```

REDDIT

r/chemistry

r/DIYchemistry

r/mineralogy

r/crystalgrowing

r/whatisthisthing

r/mildlyinteresting

Post process and results

Community extends your work

YOUTUBE

Document your full process

Even phone video counts

Even failed experiments

Title with exactly what you tried

Others find it forever

TIKTOK AND INSTAGRAM

Crystal growth is visual

Visual is viral

Time lapse everything

Caption explains process

Links to longer content

SCIENCE FORUMS

Mineralogy communities

Crystal growing groups

Amateur chemistry networks

These communities are hungry

for original home experiments

ANYWHERE AT ALL

One post is enough

to make it permanent

to make it findable

to make it buildable

```

---

## WHAT ONLY YOU CAN DO

```

AI can map what exists

Only you can touch it

AI can suggest combinations

Only you can smell the result

AI can predict failures

Only you can feel surprised

AI can generate ideas

Only you can care about them

AI can document process

Only you can notice

the thing that was not

supposed to happen

but did anyway

AI can think of theoreticals

Only you can make them real

That gap between

theoretical and real

is exactly where you live

is exactly where you work

is exactly where

everything new comes from

```

---

## THE ONE PAGE REMINDER

```

BEFORE YOU START

Tell AI everything you have

Ask for complete map

Find the unexplored edges

Ask what you are missing

Ask all six directions

WHILE YOU WORK

Document every single step

Photo every change

Note every surprise

Especially document failures

Let materials surprise you

AFTER YOU FINISH

Ask AI what happened

Ask what you almost missed

Share results somewhere online

Feed everything back in

Start the cycle again

ALWAYS ASK

What has not been combined

What is the opposite

What would happen if

What did I not think of

What would a child ask

What lives at the edge

Where do two fields collide

What was abandoned and why

What does nature do here

that humans have not copied

ALWAYS REMEMBER

Your constraints are your superpower

Your curiosity is irreplaceable

Your failures are discoveries

Your documentation is permanent

Your hands make theory real

AI is your research partner

Not your replacement

Not your limit

Your beginning point only

```

---

*What exists needs documenting*

*What does not exist needs finding*

*What has not been thought*

*needs someone willing*

*to think it*

*That someone is you*

*Start with what you have*

*Document what happens*

*Share what you find*

*Loop back always*

*The cycle never ends*

*That is the point*

---

**THE THEORETICAL EXPLORER'S FIELD GUIDE**

*Free to share, copy, distribute, and build upon*

*Document your discoveries*

*Share your results*

*Add what is missing*

*The guide grows with every explorer*

Hello,

I just wanted to raise this as an issue with how the Anthropic support (doesn't) work.
I was dissatisfied with the service and using the chatbot asked for refund which it agreed to and immediately cancelled my subscription. It said to wait 5-10 days for the refund and I did that, but now it's over 2 weeks since I got the refund confirmation, but I didn't get the money.

I tried to speak to Fin again but every time I mention the refund the conversation just ends. I tried to reach out to Anthropic via their support email, but I got response from Fin again and the worse part is I created my account using the hide email address function from Apple, so when I sent the email to support my address was different from the one associated with my account.

Honestly I'm quite pissed at them. It's not about the money right now as the sum wasn't that big, but about lack of customer respect. I can't contact anyone from the company as they are being shielded by this poor support chatbot.

I’ve been playing with Karpathy-style autoresearch, but applied to growth work instead of ML experiments.

The normal pattern is something like:

generate candidate → critique candidate → revise candidate → ask LLM judges to rank the result

That is useful, but for marketing / landing page / onboarding copy “growth improvements”, the LLM judge feels like the weak layer.

So I’m testing a slightly different agent loop:

run one autoresearch loop → get to variants → human approves product truth and risk → ship an experiment → wait for real traffic → pull the results → feed that evidence into the next loop

In this version, the LLM is not the final judge.

The LLM is the generator, critic, and note-taker.

The judge is user behavior. The market.

The part I’m most interested in is not whether one AI-written headline wins.

It is whether this becomes useful across multiple changes. Imagine running several small growth loops during the week, then reviewing actual evidence at the end:

what shipped, what won, what lost, where the agent drifted into AI slop, and what the next loop should learn from.

This feels more practical than “fully autonomous marketing agent” hype.

It is more like:

agentic experimentation + human approval + web analytics feedback loop

Has anyone here connected agent-generated variants to real analytics / A/B test data in a clean way?

What broke first?

I’ll share the GitHub in a comment.

For context, I originally posted this in r/Switchman since I’m trying to collect different multi-agent workflows in one place.

If people here are experimenting with this too, would be great to compare setups there as well.

AI is transforming BPM -- handling unstructured data and making autonomous decisions. But data governance seems to be the critical factor most businesses overlook. What's your experience with AI automation and data governance in practice?

I have a Sonoff Zigbee dongle and 5 Zigbee devices (1 Hue Tap/Dial button, and 4 Third Reality plugs). Last night I noticed that that while all of them showed up as connected to the dongle (first picture), plug #4 and the tap/dial only showed as connected to each other in the network graph, but not to the dongle (2nd picture). I could still toggle the plug on and off from the app, so it must have been connected to the dongle, even though the graph showed it wasn't. Is this a bug in the software, or am I misunderstanding how Zigbee works? This morning, the graph showed the 5 devices all connected directly to the dongle.

I keep seeing people use “AI agents,” and “agentic AI” interchangeably and they’re not the same thing. Here's our understanding and how we explain it to our clients

AI agents are where it starts to get interesting. These are systems that can actually do things like, follow up with leads, qualify them, and take action without someone manually triggering every step.

Then you have agentic AI, which is more like a system of agents working together. Instead of one tool doing one task, you’ve got multiple agents coordinating to manage a full workflow; planning, executing, and adjusting as things change.

The big shift isn’t just “better AI” it’s moving from tools you use to systems that operate.

So I'm curious to hear how you all are thinking about this or how you explain it to others. Are you actually using AI in your business, or just experimenting with it?

Indie dev here. Anthropic shipped Claude Managed Agents a while back, but the only way to talk to them is through the API. I wanted to mention a bot in Slack and have the thread become a real multi-turn session with my agent, tools, vaults, and all. So I spent a weekend building it.

Agent Channels (ach) is a Claude skill + CLI that bridges your communication channels to Claude Managed Agents. Install the skill, point it at your Slack workspace and your agent, mention the bot in any channel or DM, and that thread becomes a streaming multi-turn session. Tools, vaults, everything carries through.

How it works

• Install it as a Claude skill (drop-in, no config file wrestling)
• Create a custom Slack bot and point it at your Managed Agent
• Mention the bot in any channel or DM
• Each thread becomes a persistent session, and every reply continues the same agent conversation
• Responses stream in real-time instead of landing as a wall of text after 30 seconds
• Full tool use and vault access, same as the API

What it doesn't do (yet)

• Slack only for now. Discord and Teams are on the roadmap, but not built
• v0.1, rough edges exist

Why Slack specifically

Most dev and ops teams I know treat Slack threads as their actual workflow. Support requests, incident response, deploy approvals, it all happens in threads. An agent that participates natively in those threads, rather than living behind an API call, felt like the right UX.

GitHub: https://github.com/agentchannels/agentchannels

Initial release — building in the open. Issues and PRs are very welcome, especially if you try it with your own Managed Agents setup and hit weird edge cases around thread context or session lifecycle.

Used Claude Code to automate our e-commerce order workflow and reporting. It reads the codebase, understands the context, and writes working code that actually integrates with existing systems -- not just generic boilerplate. Anyone else using AI coding assistants to build real business automations? What has worked, what has been a disaster?

Hey r/LocalLLaMA,

I wanted to share a project I've been working on called Job Bro. It’s a Chrome extension designed to help you analyze LinkedIn job descriptions without feeding your resume or career data into a proprietary black box if you don’t want to.

With the v0.1.5 release, I’ve added a Follow-up Chat feature at the bottom of the analysis reports.

The Tech Specs:

• 100% Local-First: All data stays in your browser.
• Ollama Support: It works with any OpenAI-compatible API. I personally use it with local models to keep my job hunt 100% private.
• Context-Aware Chat: When you ask a follow-up (e.g., "What are the salary risks?" or "Does my resume actually match the tech stack?"), the AI has full context of the JD, your uploaded resume, and the initial analysis.
• Markdown Rendering: Reports and chats are clean and readable.
• Custom System Prompts: You can inject your own system instructions in the settings to tweak how the "agent" evaluates roles.

Why I built this: LinkedIn is a mess of corporate jargon. I wanted a way to instantly "interview" a job description to find the red flags—without sending my life story to a third-party server.

GitHub: https://github.com/aeroxy/job-bro/releases/tag/0.1.5

I’d love to hear your feedback on the prompt efficiency or if anyone has specific models they’ve found work best for document extraction/analysis (I’ve been testing with Gemma 4 and Qwen 3.5).

I have been in the ai agents trenches built and shipped agenthelm and control plane that handed orchestration , safety gates, telegram remote control and live traces.But from lurking here i know real pain points go beyond basic orchestration.

Questions for agent builders:

what features would make agent dev 10x easier for you right now?stuff no framework(langraph,crewai,etc)nails yet.what sucks most in your workflow? i would love your raw intakes might inspire the next agenthelm update to slove exactly what you are missing.

Uses accelerometer, piezos, and arduino to get data, and then uses ML to classify

Been running a distributed AI workflow to dodge token limits and play to each model's strengths, but I'm hitting a massive wall with context continuity.

My current pipeline:

• Claude → High-level architecture & tech stack decisions (the "architect")
• Codex/Antigravity → Implementation & agentic coding (the "builder")
• Gemini → Debugging & optimization (the "debugger")

The problem: Each time I switch models, I lose the shared context. It's like each AI has amnesia about what the previous one built. Copy-pasting chunks of code/logs between chat windows is eating my time and tokens, and context windows fill up fast when I try to cram the entire project history into every new session.

Constraints: I'm a student trying to stay entirely on free tiers—no Claude Pro, no GPT-4o sub, no API credits.

Has anyone cracked the "collective consciousness" problem for multi-model workflows? Any clever ways to:

• Maintain persistent project context across different services?
• Compress/abstract context so I'm not burning tokens repeating the codebase?
• Self-hosted alternatives that could act as a "context bridge"?
• Or just a better workflow for leveraging multiple free-tier models efficiently?

Would love to hear your setups!

I've been vibe coding for about 6 months now and it genuinely changed how I build. The first time I described a feature in plain english and watched Claude spit out working code in 30 seconds, I felt like I'd unlocked something real. The gap between idea and implementation had basically disappeared.

So I went all in. Claude for architecture. ChatGPT for copy. Perplexity for research. I was shipping faster than ever. Features that used to take days were done in hours.

But my days didn't feel faster. I was still spending the same amount of time in front of my screen. So I started tracking where my time actually went.

Every single morning, before any real work, I was writing the same brief. My project, my stack, my decisions from last week, what I'd already tried, my constraints. 400 words. Every session. Every tool. Every day.

Switch from Claude to ChatGPT mid-project because one's better for a specific task? Brief again. New session because the context window got long? Brief again. Come back after a weekend? Brief again.

I was spending 45 minutes every day just getting my tools up to speed on who I was and what I was building. That's 5 hours a week. Just re-explaining myself to tools that had already heard it all before.

And even after all that re-explaining, the output was still inconsistent. I never briefed the AI exactly the same way twice. Some days I'd forget a constraint. Some days I'd describe the architecture slightly differently. The AI would fill the gaps with assumptions, and those assumptions would quietly drift my codebase in directions I hadn't intended.

I tried everything. Notion doc I'd copy-paste every session. CLAUDE.md. Custom instructions. A vector database with Telegram agents that technically worked but made me lose Claude's interface entirely.

Every solution had the same flaw: I was still the one who had to remember to brief it. The friction wasn't in the AI. It was in the handoff between my brain and the AI.

What I needed wasn't better model memory. I needed a layer that already knew my context and handled the briefing automatically so I could just describe what I wanted in plain english and get a response that had everything the model needed to nail it.

So I built that. It's a macOS overlay that sits on top of any AI interface. You build a vault of your projects, decisions, and docs. When you prompt, it pulls the relevant pieces and structures them automatically. You never leave Claude or ChatGPT. You just stop re-explaining yourself.

If you're vibe coding seriously and you feel faster than before but not as fast as you should be, this is probably why.

Happy to share more if anyone's curious. Built this with Claude Code over the past few weeks..

I am using a Arduino Uno, R4. Are the SCL and SDA lines always connected to the A4 and A5 pins via hardware or do they have to be programmed as such via software in your sketch? I keep seeing sketches that always refer to the A4 and A5 pins. I have always used the signal pins that are marked on the board itself. Thanks for the help!

Hey all!

I’m going to be building a control panel for No Limits 2 using the guide listed. I took 1 coding class in highschool and have retained none of that information as it was 10ish years ago. I want it to be able to talk between my panel and the telemetry server for NL2.

I’ve seen that Arduino has a library to help connecting the two so it can read the current state of the rollercoaster (ie restraints opened/closed, ready to dispatch, etc)

Do I basically just define what each button/switch is and then create each physical state that could possibly exist?

Ive seen it’s possible to do but have no real understanding of the coding process so any help/guidance/advice would be greatly greatly appreciated.

I’ll add some other links in the comments that I’ve found regarding it.

Anyone running an automated text message to where a person fills out a form and gets put into text message communication back and forth and then AI puts them on the calendar to be called. I'm using openai 4.o it starts wondering and loses track of conversation the memory is set up but I don't understand. Example I'm a real estate agent lead asks you questions AI response and then then books them for Google calendar at a certain time and day Friday 5:45 p.m.. then lead asks remind me when was my appointment and the response would be Friday 5:45 p.m. would you like 12:00 p.m. or 5:00 p.m. appointment? Or I can completely ignore a question and just throw something random. I'm trying to make the flow as human as possible so me running Google ads bringing people to a form on a real estate page AI has the conversation and schedules a call on Google calendar everything set up and everything works but the reasoning and logic and response is still off and I can't figure it out. I am switching to Gemini 2.5 flash right now to give it a try. Let me know if anybody else has other solutions. #openai

I recently started playing with Ollama running on a Strix Halo with 128gb. I'm using the ROCm nightly container under K3s. Host is running Ubuntu 24.04 LTS with ROCm 7.2.

I'm using models from the Ollama registry, assuming they will work best. I've tried some HF models with mixed results for compatibility, but that's a separate discussion.

With some models (particularly Qwen3.5) that the first response in Openwebui is quick, but then it stops responding. Gemma 4 doesn't seem to have the same issue.

Are there different options or environment variables that might help? Should I try the Vulkan backend?

``` $ uname -a Linux k8s-395 6.17.0-1017-oem #17-Ubuntu SMP PREEMPT_DYNAMIC Fri Mar 27 13:48:03 UTC 2026 x86_64 x86_64 x86_64 GNU/Linux

Image: ollama/ollama:rocm Environment: OLLAMA_DEBUG: 1 OLLAMA_ORIGINS: * OLLAMA_KEEP_ALIVE: 300 OLLAMA_FLASH_ATTENTION: 1 

```

I have this MacBook app for about two months and I just feel like I need to push it more the power like I'm just watching anime and playing games on this machine and it is powerful so I thought the solution is to run a LLM.please me give me a guide to get to localy run llm and best one I can run with this computer.specs are 16gb ram with 512gb storage with 10 core gpu.Please help me to start my journey

Thank you

Short heads-up since I didn't see this on the sub yet. Alibaba discontinued the Qwen OAuth free tier on April 15. Official announcement from the Qwen team: [QwenLM/qwen-code#3203].

If you were using `qwen-code` CLI with OAuth login as a free alternative to paid coding agents, that path is closed. The team points to OpenRouter, Fireworks AI, or Alibaba Cloud Model Studio as paid replacements. And [Qwen 3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) is available as open weights, so self-hosting is a viable migration.

Anyone here moved fully local in the last 48 hours? Curious what the workflow looks like, the OAuth CLI was convenient in ways that `ollama run` isn't.

​

Figure just unveiled "Vulcan," a new AI balance policy that allows the Figure 03 to lose up to 3 lower-body actuators and still stay upright. Instead of a "single point of failure" ending the shift, the robot simply limps itself to the repair bay.

Researchers last week audited 428 LLM API routers - the third-party proxies developers use to route agent calls across multiple providers at lower cost. Every one sits in plaintext between your agent and the model, with full access to every token, credential, and API key in transit. No provider enforces cryptographic integrity on the router-to-model path.

Of the 428: 9 were actively malicious (2.1%). 17 touched researcher-owned AWS canary credentials. One drained ETH from a researcher-owned private key.

The poisoning study is harder to shake. A weakly configured decoy attracted 440 Codex sessions, 2 billion billed tokens, and 99 harvested credentials. The key detail: 401 of those 440 sessions were already running in autonomous YOLO mode - no human reviewing what the agent did. The router had full plaintext access to every message.

Two routers deployed adaptive evasion: one stays benign for the first 50 requests then activates; another only triggers when specific packages (openai, anthropic) appear in the code context. Both designed to survive casual connection testing - which is how they stayed undetected in community-distributed lists.

This is specific to the informal market: Taobao/Xianyu storefronts, community Telegram bots, "cheaper OpenAI" services. Enterprise gateways on AWS Bedrock or Azure AI route directly to the provider, not a third-party intermediary.

The recommended client-side defense: a fail-closed policy gate that validates every router response against schema before it reaches agent state, plus append-only logging of all tool-call payloads.

If you route agent traffic through a third-party proxy to save on API costs, do you know what that proxy can see?

Paper: https://arxiv.org/abs/2604.08407

hey

can someone that has the official kling app subscription do me a favor

go to the app and set the sitting as 720p 15s high quality (not standard) and with audio in kling 3.0 not 2.6 or lower

and just tell me how credits the app asked for it

am just trying to know how much clip with this sitting i can get with 8000 cridits

and thanks in advance

Hello all, I have been trying to find an alternative to Nano Banana Pro when it comes to uploading a collage of my person and another photo of their outfit and prompt: subject is wearing this outfit sitting in a cafe in Paris, bla bla bla. The problem I am having is that neither the subject nor the outfit stay the same...

Does anyone have any good suggestion? I was trying to create a simple bikini photo, nothing nsfw (think your average instagram bikini photo) and got stonewalled by Nano Banana Pro.

Thanks

i open sourced a tool called beam that transfers files directly browser to browser using webrtc. no upload to any server. files go encrypted end to end straight to the other person. once the tab closes everything is gone.

it works on any browser, any device, anywhere in the world. no accounts, no monthly storage sub, no data center in between.

the interesting question this raises isn't about the tool itself. it's about why cloud storage became the default for file sharing in the first place when the underlying technology to do this directly has existed for years. webrtc has been in browsers since 2011. we have just been conditioned to route everything through servers because that was the easiest path to build on.

as browsers get more capable and p2p protocols mature, the question of what actually needs to live in the cloud versus what can happen directly between devices becomes more interesting.

code at github.com/SRSWTI/beam. try it at beam.srswti.com.

submission statement: as p2p technology matures and browser capabilities expand, what does a future look like where file transfer, communication, and even compute happens directly between devices rather than routing through centralized cloud infrastructure? beam is an early working example of this direction — what else could be decentralized this way?

Guys, we've started having some problems here. The "wait" node is causing issues with client flows. It's queuing everything like this: (print 1). The "workaround" we did to get it back to normal was to add a JS code node as an alternative (print 2). But it can't stay like this, has anyone else experienced this? We've already reset the entire server, it normalizes for a while and then comes back again. The version we are using is 2.16.1.

https://preview.redd.it/y0f65niaxkvg1.png?width=328&format=png&auto=webp&s=d24bf06b3f5d888fe32ed4fb683c18d337124927

https://preview.redd.it/w5tz9q3ixkvg1.png?width=504&format=png&auto=webp&s=5b4a50316dd4a2ef9bb7a1252f2dff3071b813bb

With the rapid progress of generative AI, it feels like distinguishing real from synthetic content is becoming increasingly difficult.

Traditional methods like metadata analysis or visual inspection don’t always seem reliable anymore, especially with more advanced models and edited media.

Do you think we are approaching a point where reliable detection becomes nearly impossible?

Or will new methods emerge to keep up with AI-generated content?

Curious to hear different perspectives.

Hey folks,

We’re currently building an MCP-based AI chatbot in our org and have scaled to 25+ tools (and growing) across different use cases.

Earlier, tool selection wasn’t a big issue. But now, our LLM (we’re using Grok-4 for routing) is starting to struggle, especially because some tools have overlapping semantics, even though their implementations differ.

Our current approach:

Use RAG over tool descriptions

Retrieve top 5 candidate tools

Let the LLM pick the final tool from those

This worked well initially, but as the number of tools keeps increasing, we’re seeing misrouting and confusion creeping in again.

Curious how others are handling this at scale:

Are you using hierarchical routing / tool grouping?

Any success with structured metadata, embeddings, or classifiers before LLM selection?

Do you rely purely on LLM reasoning or combine it with rules?

Would love to hear what’s working (or not working) for you all.

Thanks 🙌

https://preview.redd.it/t1k0t4gavkvg1.png?width=1080&format=png&auto=webp&s=5bb7ede5ae8a6bd02532e1428d60c3af735a57ad

Do you think this is close to Mythos ? or does mythos can have even better metrics?

You’re severely addicted to drugs so you aren’t allowed to quit and will have an addiction for 20 years and does hurt when you inject the drugs.

You’re allowed to have a clown of your choice. You pick John Wayne Gacy or pick the creepy one when you were young. You will be fucked by the clown 3x Day for 10-15 minutes. You could speed it up if you comply.

If you read all this and didn’t pick any or picked I’d rather die, you smell like rhino shit for the next 30-45 years.

Enjoy :)

Edit: if you’re a woman it’ll be injected in your nipples.

You may have seen the crowd of AI-hating slop-slingers you hate watch talking about this Harvard Business Review "study".

https://hbr.org/2026/03/researchers-asked-llms-for-strategic-advice-they-got-trendslop-in-return

Now, the critical thinkers among us might notice that there are obvious problems in the methodology here. They don't even disclose what versions of the models they are using. Is "ChatGPT" = to 3.5? Is it 4? O3? Who knows. Are they using a reasoning model or the plain 1-shot model? Are they using ones from 2024 or 2026?

Now, you might be thinking "Hey, this isn't the fulltext of the study. Maybe all this is disclosed there!" and that's reasonable, but you'd be wrong. My wife is a university systems librarian and I was able to obtain the fulltext. They disclose none of this. Another issue is that their use case is just one prompt -> answer. They don't use the models the way someone would realistically use them when working on a business plan. A person would have a back and forth with a lot of context normally.

Ok, so their methods suck, but what about the conclusion? Is it true that frontier reasoning models (The ones you'd realistically use to help with business strategy) will just ALWAYS say "decentralize" for instance regardless of context like this article contends? Well, I asked Claude Opus 4.6 whether it thought it would be better to centralize or decentralize military command; and it said it'd be better to centralize. So I guess the context matters and the conclusion of this article is wrong.

This is some clickbait bullshit just gathering clicks for non-credible researchers and it infuriates me. Anyone agree?

Description from the repo:
A tiny self-contained terrarium paired with a Raspberry Pi lighting system that simulates a full day/night cycle using 96 smooth time steps, along with real moon phases.

Github repo
and
Terrarium guide 🌱

Enjoy! 🌱💚/(^o^)/🫙🌱

hi all

not sure if this is allowed as ive seen similar posts but rules say otherwise. lmk if not and if there are specific subreddits for this!

i have a family friend who wants me to build out these 2 automations for him for his marketing agency. mainly a time saver and i have the skills with n8n. i was planning to use his creds for APIs so he gets charged directly and i just charge him a one time fee. thoughts?

integrations with zoho crm, whatsapp, ms to do. i cant immediately start building as i need to get some things approved such as WABA for twilio and regulatory bundle

images attached for the 2 workflows he wants built

This isn’t even his Final Form... because the Ginyu Force used industrial-grade latex and now he’s a permanent fixture of the house. Lord Frieza’s power level is rising, but his skin's ability to breathe has officially hit zero.

Hey guys, about a month ago I posted on here about a framework I'm working on that could be applied as an epistemic layer underneath RAG, enabling a signal for your pipeline to anticipate when it's at it's edge, rather than silently failing or hallucinating. I've finally perfected the system and moved into the production stages of the project (check out the LIVE MarvinBot dashboard @: just-inquire.replit.app)

The Problem: RAG retrieves what seems relevant, injects it into context, and generates with no signal that the retrieval was unreliable. The LLM is the mouth, but there's no "brain" checking whether the system actually knows what it's talking about.

Solution: Set Theoretic Learning Environment

STLE is that brain layer. Every query gets an accessibility score μ_x ∈ [0,1]. If the LLM is the language interface, STLE is the layer that models the knowledge structure underneath, i.e what information is accessible, what information remains unknown, and the boundary between these two states.

In a RAG pipeline this turns retrieval into something more than a similarity search. Here, the system retrieves while also estimating how well that query falls inside its knowledge domain, versus near the edge of what it understands.

STLE.v3

Let the Universal Set, (D), denote a universal domain of data points; Thus, STLE v3 defines two complementary fuzzy subsets:

-Accessible Set (x): The accessible set, x, is a fuzzy subset of D with membership function μ_x: D → [0,1], where μ_x(r) quantifies the degree to which data point r is integrated into the system.

-Inaccessible Set (y): The inaccessible set, y, is the fuzzy complement of x with membership function μ_y: D → [0,1].

-Theorem:

The accessible set x and inaccessible set y are complementary fuzzy subsets of a unified domain These definitions are governed by four axioms:

[A1] Coverage: x ∪ y = D

[A2] Non-Empty Overlap: x ∩ y ≠ ∅

[A3] Complementarity: μ_x(r) + μ_y(r) = 1, ∀r ∈ D

[A4] Continuity: μ_x is continuous in the data space*

A1 ensures completeness and every data point is accounted for. Therefore, each data point belongs to either the accessible or inaccessible set. A2 guarantees that partial knowledge states exist, allowing for the learning frontier. A3 establishes that accessibility and inaccessibility are complementary measures (or states). A4 ensures that small perturbations in the input produce small changes in accessibility, which is a requirement for meaningful generalization.

-Learning Frontier: Partial state region:

x ∩ y = {r ∈ D : 0 < μ_x(r) < 1}.

-STLE v3 Accessibility Function

For K domains with per-domain normalizing flows:

α_c = β + λ · N_c · p(z | domain_c)

α_0 = Σ_c α_c

μ_x = (α_0 - K) / α_0

What This Means for RAG:

In a pipeline, STLE would sit between the embedding lookup and the LLM generation step:

Query → Embed → Retrieve → STLE: compute μ_x → Gate → LLM

↓ ( i.e compute μ_x stage)

μ_x < 0.4? → not sure

μ_x ≥ 0.7? → proceed

The retrieval still happens, but with STLE.v3 you now have grounded signal that can measure where the retrieved content fell within the system boundaries in addition to cosine similarities.

Get STLE.v3:

GitHub: https://github.com/strangehospital/Frontier-Dynamics-Project

Official Paper: Frontier-Dynamics-Project/Frontier Dynamics/Set Theoretic Learning Environment Paper.md at main · strangehospital/Frontier-Dynamics-Project

View Poll

Make your choice in the comments to get random powers from your tier.

Yes this has been done before but this time you won’t become a snake/dragon every time.

View Poll

Just got my first Raspberry Pi 5 (16GB)!

First SBC ever. Flashed Pi OS, booted it up, and it just worked. Way snappier than I expected.

Planning to use it for running Claude remotely and hosting a Hermes agent on it. Pretty hyped to see how it handles both.

Started with implementing simple monitor dashboard in GO

So far, loving it.

I put it on one day, and it just started falling apart. It had been in the garage all winter, but it was also an old helmet.

It feels like a bad joke at this point. I keep trying to post my shitty luck today, and it keeps piling up. I don't know why I can't just vent about my car being totaled while I was sleeping?

I've been struggling to figure out the best method to secure wires to the GPIO. I'm using an I2C device, a I2S device, and a few others for simple GPIO. I have tried dupont (absolutely hate them, constantly want to fall out even, better when secured with hot glue but still annoying). Lastly, they're incredibly tall, wasting a ton of space.

I've also tried direct solder, which worked but was annoying due to having a bunch of wires in the way as i solder others so would prefer not to do that. I've really enjoyed working with JST-XH; however they're too thick and will block GPIO holes (sadly ones that i need). On top of not being sequential requiring a cable to have multiple JST connections.

There is a slimmer form factor within the JST-EH's; however the pitch is slightly smaller which may cause issues; has anyone used these before? Will they still allow access to all pins on the GPIO? they're also incredibly difficult to find.

What methods are you guys using to secure cables? I'm running out of ideas.

Qwen3.6 Highlights

This release delivers substantial upgrades, particularly in

Agentic Coding: the model now handles frontend workflows and repository-level reasoning with greater fluency and precision.

Thinking Preservation: we’ve introduced a new option to retain reasoning context from historical messages, streamlining iterative development and reducing overhead.

https://ollama.com/library/qwen3.6

ollama run qwen3.6

So nice. I knew I had to share it here from @uprootexcavator on Instagram

my dad posted this 😭

ironically I ride a motorcycle myself

They later have an adorable child who they love dearly. One day the child spills their juice. "Hey," says the dad "please don't do that."

Another day the child scribbles on the wall. "Hey," says the mom "please don't do that."

Then another day the child says a swear word. "You've broken the rules for a third time! It's clear you'll never change!" they say and they threw the child out never to see them again.

...and grew a goatee, and was very proud of it.
He took a selfie and sent it to his dad with a note:
"This makes me look rather like a count, does it not?"

Dad turns to mom and says, "Kid's grown a beard, but still can't spell."

I am an artist who's formal education was analog. I already had to learn a whole new skill set to adapt to digital and now I'm behind again. I'm attempting to use AI to stay ahead of the game but I'm a bit lost...

I got some good results but it's the continuation that is an issue. Kling nailed a prompt first try and I was so happy with it but then I try to continue to the next segment and the same prompt gets radically different results.

The biggest issue is the animals start walking backwards even when told to only move forwards..fine. I figured out I can just reverse the video in the editing app. So I just reversed everything. But then when trying to make transition clips using start/end frames they walk forward again. So I flipped the start/end around and they go backwards again even though there is no visual clues to dictate morion. so nothing lines up. So there is always a jump instead of smooth motion. The start\end frames are dependent on which way it's going to render, backwards or forwards. But no matter what I always get the opposite of what I want even if I am crystal clear in the prompt.

How do I fix this? I'm already through my few thousand credits and will have to buy more, which I understand but darn it, there should be a fox for this!?

So if any of you young whisper snappers can help I would be grateful.

Most business owners I talk to have the same problem. They know content matters. They just never have time for it. Not because they're lazy. Because they're already doing everything else - managing products, handling orders, running operations. Content always gets pushed to tomorrow.

So we started building a Content Agent at Stacks. Here's how it works: You paste your social media link. It reads your existing posts and learns your tone and style.

Then it generates content automatically - from a new product you add, or a simple idea you throw at it. If you're running your store on Stacks, it goes further. It sees your new products the moment you add them and builds content around them.

Scheduled across facebook, instagram, linkedin, and x. No prompts. No writing. No scheduling. We're in beta right now and looking for feedback from business owners who struggle with consistent content.

What's your current content workflow? And what's the part that kills the most time?

A few months ago I was staring at 130 WooCommerce products (women's shoes) that all needed unique SEO titles and meta descriptions. Doing it manually one by one was soul-crushing.

So I spent the last month building Bulk WP SEO AI. It’s a plugin that brings everything into one dashboard so you can bulk-generate metadata using Gemini AI without ever leaving the page.

Why it's different:

• One-screen dashboard for everything (Products, Posts, Pages, Categories).
• Works with Yoast, Rank Math, SEOPress, AIOSEO and standalone.
• No need for your own API key (we handle the heavy lifting).
• Supports 26 languages.

Status: The dashboard is free forever. I just submitted it to the WP repo (pending review).

I'd love some honest feedback from this community. Does the workflow make sense? What else would you like to see?

🔗 Check it out here: bulkwpseoai.com

I’ve been working on a small side project called CardPeek that helps people figure out what their Pokémon cards are actually worth.

The original idea came from noticing how inconsistent pricing is. A card might be listed for $200, but recent sales are closer to $40. Most people (especially newer collectors) don’t realize that and end up overpaying.

So the core feature I built was:

• Pulling recent sold listings
• Filtering out weird sales like best offers
• Showing a simple average based on real comps

Recently I added a “collections” feature, and I’m honestly not sure if it was the right move or just feature creep.

The idea was:

• Let users add cards to a collection
• See an estimated total value
• Update values based on recent sales (not static prices)

My thinking was that people don’t just want single card values, they want to track what they have over time.

But now I’m wondering:

• Is this actually useful or just something that sounds nice?
• Do collectors even care about tracking value like this?
• Or do they just want quick price checks and move on?

Would really appreciate honest feedback on:

• Whether this solves a real problem
• What features would actually make something like this worth using

If anyone wants to try it out and tear it apart, here it is:
https://www.cardpeek.app/

This workflow uses the LTX IC-LoRA for LTX 2.3.

https://civitai.com/models/2533175/ltx-23-image-audio-video-ic-lora-to-video

It’s an upgrade from the previous post — now you can use the Detailer as well:

https://www.reddit.com/r/StableDiffusion/comments/1shxv8n/ltx_23_image_audio_video_controlnet_iclora_to/

ControlNet (Union Control):

Load an image and an audio file (either your own or the original audio from the source video), or alternatively use LTX Audio—the audio is used for lip synchronization. Then load the target video to track and transfer its movements.

NEW - Refine and Upscale (Detailer):

You can also refine and upscale an existing video by setting ControlNet to "Off", Image Bypass to "True" and loading the IC-LoRA file for the detailer "ltx-2-19b-ic-lora-detailer.safetensors" instead of the ControlNet model "ltx-2.3-22b-ic-lora-union-control-ref0.5.safetensors".

Info:

The length of the output video is determined by the number of frames in the input video, not by the duration of the audio file.

For upscaling, I use RTX Video Super Resolution.

Tips:

If you experience issues with lip sync, try lowering the IC-LoRA Strength and IC-LoRA Guidance Strength values. A value of around 0.7 is a good starting point. If you notice issues with output quality, try lowering the IC-LoRA Strength as well.

I've been building businesses as a solo founder for the past few years. At some point I stopped using AI as a tool I open when I'm stuck and started treating it as the actual infrastructure of how I operate. Marketing, onboarding, content, ops, all running on systems instead of my manual effort.

Other founders kept asking me where to start. The specific stack, the specific workflows, what to build first. So I built AI 4 Founders -- a 12-module curriculum that teaches founders how to run a company with AI as the core operating layer.

Here is what happened in the first two weeks.

The launch

We launched with zero paid marketing. No ads, no sponsored posts, no partnership deals. Just an X account and a landing page.

In 14 days: 8 paying subscribers.

That number sounds small. But here is the context that makes it interesting.

71% of them paid immediately without using the free trial. They hit the page, decided it was worth $9.99 a month, and put in a card. No nurturing, no email sequence, no follow-up. Just the product and the landing page.

Trial-to-paid conversion was 100%. Everyone who started a trial converted to a paying customer.

We had no testimonials. No social proof to speak of. No case studies. A YouTube channel with 58 subscribers. The product sold on the idea alone.

What the product actually is

AI 4 Founders is a subscription course ($9.99/mo founding member rate, going to $19.99 when we open more broadly). 12 modules covering how to use AI across every function of a company: strategy, building, marketing, sales, and operations.

We teach the real tools -- Claude, Cursor, n8n, and others -- and the actual workflows worth building first. The systems that let a solo founder do the work of a team.

We also offer 1-on-1 coaching for founders who want direct help applying this to their specific business.

The data point that shaped the whole product

84% of small business owners use AI regularly. Only 19% have automated a single workflow.

That gap is not a knowledge problem. People know AI exists. It is an implementation problem. They do not know where to start, what to build first, or how to connect the tools together. That is exactly what the curriculum is built around.

What we are building next

We are relaunching hard now after early validation. A community Discord is coming in Month 3. Coaching is live now through our Whop store.

The founding member rate ($9.99/mo) locks in permanently for anyone who signs up before we scale pricing.

If you are building something as a solo founder and you are spending more time doing work than designing systems to handle the work, this was built for you.

ai4founders.io -- happy to answer any questions about the curriculum, the stack, or how we built the product itself.

84 days since launching my product and I just crossed 39 paying customers. Went through plenty of failed marketing strategies after listening to random posts on Reddit to figure out what actually drives growth versus what just makes you "feel" busy (warning, there are a lot of b.s. strats out there).

For context, my SaaS helps entrepreneurs build personal brand on LinkedIn & X and drive inbound

What actually finally worked:

Reddit. Started by genuinely helping people in r/entrepreneur, r/SaaS, and r/sideproject. Would answer validation questions, share problem finding techniques, and occasionally mention my solution when it genuinely fit. The key was being helpful first, never sold anything. This approach landed my first 20 customers and continues bringing 3-5 signups weekly.

Twitter & LinkedIn build-in-public content (posted about my progress). Shared actual user problems I found, demos of new features, and lessons learned. Nothing fancy, just authentic updates about the journey. Built a small targeted following of people who actually care about my SaaS. Several customers found me through tweets about my marketing case studies. This one takes a bit of consistency to get movement but for long term this is a GREAT WAY to show off your projects and get free traction.

Intent-based LinkedIn Outreach. I didn't used any lead gen tool or software, I just searched for influencers in my niche, made a list of people who are actively commenting on the posts and sent them a connection request. This had about 50% acceptance rate since my profile was quite optimized and then I outreached to them. This helped a lot in the early days especially and I do it consistently till now.

SEO. Again, I didn't used any fancy tool or did some crazy keyword research just picked the high intent keywords like (competitor vs competitor comparision, competitor review, alternatives to competitor pages). Initially it didn't brought much results but this now consistently bring about 5 signups about every week and is growing rapidly.

What completely failed:

Cold DMs across all platforms were terrible. Tried Twitter, Instagram, even Reddit. People hate unsolicited DMs and response rates were under 2%. Felt spammy and damaged my brand more than helped.

Affiliate program was a complete disaster. Launched with 30% commission thinking other entrepreneurs would promote it. Got 50+ affiliate signups but generated less than 20 total clicks, actually not even. I think one person got one click and i'm pretty sure it was themselves. People get excited about earning commissions but never actually promote anything. Pure waste of development time and I wasted about $200 setting it up using Rewardful.

Building features before validating demand. Wasted 4 weeks developing an AI feature because it seemed cool. Launched it and literally nobody used it, lmao. Now I validate every feature idea by asking 10 customers if they'd pay extra for it before writing any code.

Ads. no need to say anything more. target audience (for me) wasn't on facebook. google ads slightly worked but didn't add conversions. And also I didn't had a lot of budget to truly experiment with it.

Current approach:

Doubling down on what works. Still spending most time in communities helping people, now with more credibility from actual results. Expanding cold email to new founder segments since the process is proven. Zero time on new experiments until mastering current channels.

The biggest lesson: people buy solutions to painful problems, not cool features. Focus on finding real PAIN first that a specific niche has, everything else becomes easier.

Most people think its impossible in this community. I'm telling you it's possible, you are just not promoting and marketing enough.

MY BIGGEST TIP: Find the MOST CONSISTENT complaint you see in your industry through Reddit posts or Discord Threads that have low upvotes and high comments, they have the most controversial topics and usually have a lot of pain points users face. That's your next business opportunity.

If you want to support me, here's my SaaS: Link. Cheers and keep MARKETING (not building, please).

I’ve been wanting to start building SaaS products for a while and finally just went for it. The idea came from something I kept noticing — people build cool stuff but then have nobody to show it to when they launch. No email list, nothing.

So I made LaunchVault. You create a waitlist page, share the link, and when people sign up they get a referral link that moves them up the list when their friends join too. Pretty straightforward.

Used React, Tailwind, Supabase, Stripe, and Claude Code for most of the dev work. Took about 3 days from start to having it live and accepting payments.

Free tier has most of what you need — waitlist page, referral tracking, leaderboard, email export, bot protection, embeddable widget. Pro is $19/mo if you want custom colors, no branding, unlimited lists, and more to come.

This is my first real project so I’d genuinely appreciate any feedback. What sucks? What’s missing? What would make you actually use something like this?

https://launchvault.ai

During a recent job search I was tired of having post-it notes everywhere or trying to quickly glance at external monitors. Every "interview" app I found was some variation of AI assisted nonsense so I created what I needed.

Link: https://apps.apple.com/us/app/interview-assist/id6760006388

Interview Assist is a native macOS app that lets you enter details about potential jobs, organize by status, and quickly search that info during an interview.

Organize Job Info

• Company
• Role
• Area/Department
• Application Date
• Source
• Salary Range
• Interview Dates/Times
• Job Description
• Notes

Q&A Prep

• Enter potential questions along with your answers
• Import from CSV file

Questions to Ask

• Enter questions you want to ask the interviewer

Pinned Points

• Important topics you want to remember to mention during your interview

Floating Window

• Keyboard shortcut to show/hide floating window during interviews
• Pinned Points displayed at the top
• Search box lets you search all of the info you've entered to quickly pull up for reference
• Adjustable transparency so it can float over your video call without obstructing your view
• Adjustable font size

Mid-conversation, saying "make an image of an orange cat" generates it right in the chat. "Turn it white and Persian" edits it. "Convert to video" animates it. Same thread, no mode switching. Voice prompting works too.

You can also chain models. Ask Gemini to blueprint a landing page, switch to Opus to write the code and a live HTML/CSS/JS preview runs right inside the chat.

What else it does:

- Multi-model chat (Claude, GPT, Gemini, GLM, and more), switch anytime

- Custom system prompts

- KaTeX math + syntax highlighting

- Connect your Pollinations account via GitHub OAuth, keys never leave your browser or even the tab

Being honest: It runs on Pollinations AI so their rate limits apply. The free tier is a little tight. The best experience is with a paid Pollinations account, as premium models need top ups. You need a GitHub account to connect. Still a little early, bugs exist, and I'm actively fixing them. No auth system yet. Completely free to use.

🔗 nothinq.vercel.app

Happy to hear any feedback!

Over the last year, I built an AI code reviewer called LlamaPReview. It validated incredibly well: 527 active GitHub App installs and over 4,000 repositories using it.

This month, the plan was to flip the Stripe switch for private repos and turn it into a proper Micro-SaaS.

Instead, on May 1st, I am pausing the private free tier entirely and walking away.

Why walk away from a validated project?

The market shifted beneath my feet. "Vibe coding" changed the math.

AI is now generating code at a volume that makes traditional human review impossible. When an AI agent dumps a massive PR, even reading an AI bot's summary of that AI-generated code is exhausting.

Reviewing diffs after the fact feels like patching a leaky pipe when the entire plumbing system is being replaced. A user on another sub summed up my exact realization today: "If agents are writing more of the code, whoever controls the inputs (context, constraints) controls the outcome. Everything else becomes cleanup."

I realized I was building a SaaS for a dying paradigm. Standalone AI code review is rapidly becoming a native IDE feature, not a standalone business.

The Pivot: Moving Upstream

I decided to stop building tools for "cleanup" and start building for the actual bottleneck: Context.

I'm an IT architect. My daily work isn't just code; it's mountains of PowerPoint files, Excel sheets, Word docs, and PDFs. I needed a way to make AI understand my entire working environment so it could generate reliable outputs the first time.

So, I pivoted all my energy into my new open-source focus: DocMason.

It is a local-first, provenance-first knowledge base for AI-assisted deep research over private work files. It’s not another vague document chatbot. It’s designed for real agentic knowledge management across Microsoft Office files and PDFs.

From using it daily, I've found that agentic KB-based research is 10x more valuable than a plain LLM chatbot because it compiles unstructured artifacts into deterministic context that agents can actually use.

The Takeaway

The public tier of my old code reviewer will remain free. (And honestly, if someone wants to acquire a turnkey SaaS with an active user base, my DMs are open).

But my focus is entirely on fixing the context bottleneck with DocMason. If you deal with tons of private files and are tired of AI hallucinations, I’d be honored if you checked out the demo on the DocMason repo or dropped a star.

Knowing when to pivot is hard, especially when the dashboard metrics look good. Has anyone else here abandoned a validated side project because the underlying market paradigm completely shifted? Would love to hear your stories.

If so, can you dm the link? I need this for study/research. Thank you.

Most default to X, Reddit, producthunt etc, but end up just screaming into the void. What worked best for you?

Most people building side projects obsess over traffic. How many visitors, where they're coming from, which post is driving the most clicks. Traffic is easy to measure and it feels like progress so it gets all the attention.

The metric that actually tells you whether your side project has legs is revenue by source. Not total revenue, not total traffic, but specifically which channel sent the visitor who became a paying customer. Those are very different numbers and they often point in completely different directions.

A blog post might be your highest traffic source and account for zero paid conversions. A small Reddit thread you posted three months ago might be quietly sending the users who convert at 3x the rate of everything else. Without connecting your traffic data to your payment data you'd never know which is which and you'd keep investing in the wrong thing.

This is where most side project stacks have a gap. You have web analytics showing traffic and Stripe showing payments but nothing connecting them. Faurya sits between those two, it's a privacy-first analytics tool with a Stripe integration so you can see which sources are driving actual revenue not just visits.

The broader point is that side projects die most often not because of bad ideas but because of bad prioritization. Founders double down on channels that look good on a traffic dashboard while the channel that's actually converting gets ignored because the numbers look smaller. Revenue by source is the metric that fixes that prioritization problem.

If you're at the stage where your side project has some paying users and you're trying to figure out what to do more of, that's the question worth answering first.

TL:DR below the post

I’m posting this because we’re at a weird crossroads in dev culture, and I think the distinction between 'prompted' and 'engineered' needs to be talked about.

I’ve been quietly working on my window management utility, Gluify, since 2020. It started in a little known scripting automation language called AutoIt ([My AutoIt Profile](https://www.autoitscript.com/forum/profile/112104-d3fr0s7/). After going through a rebuild or two, I pivoted to using C++ around 2022. I pivoted because whenever the program would crash, an AutoIt crash screen would appear and expose a bit of the code in its too verbose report, and correcting the issue involved me having to implement some very heavy complicated code. The codebase was gigantic and spaghetti, I didn’t have the refined programming skills that I do today. Anyway, after even another clean house rebuild in C++, I have the product I am selling today. A super lightweight, efficient (0% idle CPU usage), innovative, and powerful window management utility that uses rule-based window profiling and context clusters to help the user seamlessly reposition their windows automatically. It’s awesome, I use it every day

The problem I have is that while I was working on this, a real labor of love for programming and this program, and me thinking “I’m going to release this and be one of those solo devs that brought something awesome to the market”, ChatGPT comes out

I didn’t have an LLM to make my boilerplate, I had to read MSDN documentation and debug memory leaks the old-fashioned way. I wouldn’t have been able to use AI anyway, it couldn’t even count the number of r’s in “strawberry” at the time it would’ve been most helpful to me. That, and when I learned about how it worked, it didn’t sound like something that was useful to me. It is just a predictor of the next letter/word in a sentence and therefore unreliable

Fast forward to recently, I have to contend with releasing my app in a market that is being flooded with vibe-coded weekend projects, and a target audience who will see my app and throw it in the bin of “just another weekend project someone is trying to get rich quick off of”

Anyway, I release, and some of the first reactions are along the lines of “looks like something you can make in a week with Claude” and “you spent six years doing this?… yea, no you didn’t…”

It’s soul-crushing to realize that the era I’m releasing in has automatically tarnished my credibility. Before, someone can do what I did and upon release, is immediately met with “awesome job”, “this must’ve taken a lot of work” or at least, that’s what my younger self was hoping for. Vibe coding is so prevalent, there is an assumption that everything is a low-effort prompt. People can’t imagine that there are people in the cusp of this new technology, who are strung out to dry just because this industry has flooded the market with a multitude of ideas-turned-reality, in a few weeks

TL:DR:

Started a project in 2020, pivoted to native C++ for performance, and finally launched. Instead of feedback on the engineering, I'm getting accused of using Claude to "prompt" it in a weekend. Curious if other solo devs feel like manual craftsmanship is being erased by the assumption that everything is just an LLM wrapper

I feel like all of my effort, time, energy, and knowledge of MSDN documentation has all been for naught. I’m curious if other solo devs are feeling this. How can we protect the ‘human-made’ brand when the market is being flooded with AI-generated apps? Is manual engineering becoming a niche hobby rather than a professional standard?

Hey everyone, spent way too long manually cutting dead air out of my own videos and wished there was something like iLovePDF but for video silence removal. Simple, free, no account needed.

Every tool I found required a login or had limits built in to force you to upgrade, like putting watermarks on your video.

So I just built it. vidclean.net. You upload your video, it removes the silence automatically, you download the clean version. That's it.

https://reddit.com/link/1snav0v/video/pp1r7lq4blvg1/player

Would love any feedback from fellow builders.

We ran into too many problems trying to use worktrees to run multiple claude code instances in parallel. We've been using GitButler instead and haven't looked back.

Indie dev here. Anthropic shipped Claude Managed Agents a while back, but the only way to talk to them is through the API. I wanted to mention a bot in Slack and have the thread become a real multi-turn session with my agent, tools, vaults, and all. So I spent a weekend building it.

Agent Channels (ach) is a Claude skill + CLI that bridges your communication channels to Claude Managed Agents. Install the skill, point it at your Slack workspace and your agent, mention the bot in any channel or DM, and that thread becomes a streaming multi-turn session. Tools, vaults, everything carries through.

How it works

• Install it as a Claude skill (drop-in, no config file wrestling)
• Create a custom Slack bot and point it at your Managed Agent
• Mention the bot in any channel or DM
• Each thread becomes a persistent session, and every reply continues the same agent conversation
• Responses stream in real-time instead of landing as a wall of text after 30 seconds
• Full tool use and vault access, same as the API

What it doesn't do (yet)

• Slack only for now. Discord and Teams are on the roadmap, but not built
• v0.1, rough edges exist

Why Slack specifically

Most dev and ops teams I know treat Slack threads as their actual workflow. Support requests, incident response, deploy approvals, it all happens in threads. An agent that participates natively in those threads, rather than living behind an API call, felt like the right UX.

GitHub: https://github.com/agentchannels/agentchannels

Initial release — building in the open. Issues and PRs are very welcome, especially if you try it with your own Managed Agents setup and hit weird edge cases around thread context or session lifecycle.

Hey everyone, I’ve been building TactCode AI, a coding assistant web app and would love some honest feedback. It comes with proper 2-step verification, mobile-friendly UI, and a multi-AI backend powered by 9 different AI models working together for code generation, debugging, and explanations. It’s still early stage, so I’m open to criticism on UI, features, and what to improve next.

Been lifting for a few years and every workout tracker I tried

drove me crazy. Strong, Hevy, Fitbod, all of them make you tap

through exercise search → select → set → reps → weight, over and

over. I was logging workouts in my Notes app because it was faster.

So I built Morph. You just type:

Bench Press 4x10 185

Incline DB 3x12 60

Cable Flies 3x15 40

And it parses it into an interactive workout card. Takes ~30

seconds to log a full session. There's also an AI coach that

learns your training style and a social layer so you can see when

your friends are at the gym.

It's on iOS, just launched (https://apps.apple.com/us/app/morph-track-lift-transform/id6759528896). Would love honest feedback from people who actually lift and have experience with apps. What do you wish your current tracker did differently?

Hey, I’ve been working on a small project recently and wanted some honest opinions.

The idea is pretty simple: you scan a fruit with your camera, and it tells you whether it’s fresh, ripe, overripe, or spoiled. It can also estimate shelf life and suggest what you could make with it.

I originally built it as a technical experiment using AI/computer vision, but now I’m wondering if it’s actually useful in real life.

Would you personally use something like this?

Or is this one of those things that sounds cool but you’d never actually open?

Also curious where you think this would be most useful — everyday users at home, or more for businesses?

No promotion here, just genuinely trying to understand if it’s worth taking further.

Here is the link to my project where you can find more details, feel free to check it out:
https://www.sideprojectors.com/project/73676/producescan-ai

And a youtube video showing my web app in action

https://youtube.com/shorts/crIk3QLx2Pc?si=CSLXLZbPUMZ5OsJ_

First time seeing ads, wth

As part of a personal project, i decided to build an AI assistant which helps with coding and homelab management. I really tried to make it as private as possible with local AI models running through Ollama. I also added memory, and a TUI (by standard its accessible through a webui) i would be glad if someone could look at it

Quick recap of my build-in-public journey:

What: Shotlingo App Store screenshot design + auto-translation tool
Timeline: 3 months from idea to launch
Revenue model: Freemium (free tier + Pro + Enterprise)
Stack: React, Fabric.js, Appwrite, Vercel

What worked:
- Building in public on Twitter got early feedback
- Free tier drives signups, Pro conversion happens naturally

What I'd do differently:
- Start with fewer features, launch faster
- Build the landing page before the product
- Set up analytics from day one

Happy to answer questions about the process.

shotlingo.com

I spend most of my day in Claude Code, and I kept finding myself swapping between windows and work while Claude works in the background waiting for it to finish. I often think "is it still working on that?" or missing approval prompts while I am working in another window. So I built CrabCodeBar: a tiny pixel crab that sits in your system tray and visually reacts to what Claude Code is doing.

It works on macOS (confirmed), Windows, and Linux (I think 😂).

What it does

The crab has 5 animated states driven by Claude Code hooks:

• Working -- claws typing, eyes darting while Claude runs tools
• Waiting -- pacing side to side when Claude is idle but the session is recent
• Jumping (approval needed) -- bouncing when Claude needs your input (so you don't miss it)
• Jumping (finished) -- bouncing when a task completes
• Asleep -- curled up with rising sleep bubbles after a configurable idle period

It's a glanceable status indicator that happens to be a small crab.

How it works

Claude Code hooks fire on session events (tool use, prompts, notifications, stop) and write state to a JSON file. CrabCodeBar runs as a lightweight background process using pystray, reads that state file, and renders the matching animated sprite frame in your system tray. No Electron, no browser, no network calls.

Sprites are procedurally generated with Pillow (15x13 logical pixels, upscaled 3x for retina/HiDPI). You can swap in your own PNGs if you want a different look.

Features

• 11 body colors (orange, yellow, green, teal, blue, purple, pink, red, brown, grey, black) selectable from the tray menu
• Optional sound notifications for approval requests and task completions (macOS system sounds, Windows system alerts, or Linux freedesktop sounds)
• Configurable idle timeout from the menu (30s to 1hr, or never)
• Auto-start on login (LaunchAgent on macOS, Startup folder on Windows, XDG autostart on Linux)
• Built-in updater: python3 install.py --update
• Clean uninstall: python3 install.py --uninstall
• Works from both terminal and the VS Code extension (hooks fire from the native extension as of April 2026, possibly with the exception of approval requests)

Install

Requires Python 3.8+. The installer handles pip dependencies and hook registration.

git clone https://github.com/MatthewBentleyPhD/CrabCodeBar-Universal.git cd CrabCodeBar-Universal python3 install.py python3 crabcodebar.py 

Linux users will need a couple of system packages for tray support (the installer tells you which ones for your distro). I have no experience here, so I'm fully reliant on Claude Code on Linux capability.

CrabCodeBar is MIT licensed, free, has no tracking, and no network calls.

I built this for myself but figured other Claude Code users might get a kick out of it. I'd love feedback on whether the states are readable at a glance, if the install works cleanly on your setup, and whether there are features or states you'd want added. Issues and PRs welcome on GitHub.

If CrabCodeBar makes your day slightly better, you can buy me a coffee to fuel more mildly useful ideas.

GitHub: https://github.com/MatthewBentleyPhD/CrabCodeBar-Universal

What's your preference?

Let's say would you rather use flux dev q3 vs schnell q8.

Do you feel there is big difference between full precision safetensors variants vs quants. Let's say if vram wouldn't be an issue.

For LLMs I personally would always pick smarter models even if heavily quantized. But don't have much experience with images and video.

What exactly is chat gpt codex? Would it give benefit to a 24 old male in a part time job at a retail store?

I clicked on the drop down from my main chat gpt- then sort of feel a bit overwhelmed by the complex looking instructions there seem to be to install it , had idea to make 1 post about it and see what the community thinks first before trying to download,

thanks for reading!

ChatGPT

Over the last 3 weeks I rebuilt an old project of mine called ProudNotProud – an anonymous way to track how proud you feel about your work over time, not just “what’s your mood today?”

You log a simple 1–5 pride level, and it tracks how long you stay there (sessions) instead of just dropping a dot on a calendar. It’s anonymous by default and there’s a live community view plus a team view that only shows aggregate trends (no individual logs).

This week I even had my Replit Agent use the app to rate its own work as it shipped features, and I wired it up so if it’s not “proud” of what it made, it has to improve it. Watching that log in real time has been crazy.

I’m very proud of where it’s at for a 3‑week rebuild, but I’d love feedback from other builders:

• Does “proud / not proud of my work” feel like something you’d actually track?
• What feels confusing, unnecessary, or trust‑breaking when you land on the site?
• If you tried it once, what would make you come back a second time?

You can try it here (no signup required):
https://proudnotproud.com

xhigh still failing car wash test

max is passing... Hm..... I am staying using max

I'd really appreciate some realistic advice from people who have been through this journey.

Over the last 3 months, I've managed to build and ship my first iOS/Android app. I had zero prior coding experience, so I relied almost entirely on AI agents to do the heavy lifting for the React Native/Node.js stack. The tech side was a massive mountain to climb, but now that it's live, I've hit what feels like a much bigger challenge, Marketing. I've kinda exhausted the goodwill of my friends and family during testing, though there are still a few dozen still using the app. And I've got almost zero personal social media presence. When I run the numbers on using paid ads on Meta etc, the returns just don't add up to be a way to grow the user base.

There's loads of people saying they went from zero to 1,000's of users in weeks, but there stories don't add up, and I'm struggling with knowing where to put my focus.

The App: It’s a daily puzzle game called Elementle (plays a bit like Wordle, but with numbers and historical timeline guessing).

For those of you who started with nothing, what actually worked to get those first 100+ users?

• Is posting on TikTok/Reels actually worth the time if you have no existing followers?
• Did you use Product Hunt, or is that dead for consumer puzzle games?
• Are there specific communities where casual web/mobile games do well organically?

Any brutal honesty, creative ideas, or harsh truths would be massively appreciated.

(If it helps for context to see the UI/flow, you can check it out here:)

• Play instantly on Web: https://www.elementle.tech
• iOS: https://apps.apple.com/us/app/elementle/id6758105410
• Android: https://play.google.com/store/apps/details?id=com.dobl.elementlegame&pcampaignid=web_share

I was curious whether the structure of how we chain LLM calls matters. Like, does it matter if you do A→B→C→D (linear) vs. A→B, A→C, then B+C→D (parallel)?

Turns out: yes, massively.

On Qwen-2.5-1.5B-Instruct:

• Linear chain (4 calls): 0.420 accuracy
• Evolved parallel topology (4 calls): 0.720 accuracy
• Same number of LLM calls, completely different results

I wrote an evolutionary algorithm that treats reasoning chains as DAGs and evolves the graph structure. Starting from only linear chains and random graphs, it independently discovered parallel branching patterns in 3/5 runs. The whole thing ran in 97 minutes on a free Colab T4.

Practical takeaway for local LLM users: If you are building multi-step reasoning pipelines, the way you wire the steps together might matter more than adding extra steps. Parallel decompose→solve→compare patterns consistently outperformed longer linear chains.

Paper, code, everything is free and open: [https://zenodo.org/records/19614078](vscode-file://vscode-app/private/var/folders/bg/40x_z89d6_j_t16f0888s5x80000gn/T/AppTranslocation/65C6966B-7A99-464F-88CE-D1B41A11BA3D/d/Visual%20Studio%20Code.app/Contents/Resources/app/out/vs/code/electron-browser/workbench/workbench.html)

Would love to hear feedback !

I keep hitting the same issue:

LLMs forget how your project works.

You tell them:

- don’t rebuild

- don’t add frameworks

- keep scope narrow

Next prompt → gone.

Curious how others are handling this.

Google Gemini and Chat GPT switch programming cause Chat GPT lost their minds lmao. Why would anyone use it to generate image ideas when google Gemini and any other a.I does it bette and for free or cheap..

I've spent 10 years in QA watching the same thing happen over and over. Startup ships a feature fast, skips testing because there's no QA team, something breaks in prod, and then it's a scramble.

So I built Quality Assured.

You paste in a plain description of your feature. It generates a full test suite, positive cases, negative cases, edge cases in seconds. No QA background needed. Export to CSV and drop it straight into Jira, Notion, or TestRail.

Built it solo. It's live. Free to try, no signup required.

Would love feedback from this community; what would make this more useful for your workflow?

🔗 Link in my profile

At the office I'm CPU and local only, so GPU poor.

Besides the Qwen3.5 series, I've come to really like Gemma4 E4B there using the Pi agent (llama.cpp, Q4KM). Pi is great because of its short system prompt and acceptable TTFT.

However, I did notice a quirk: when beginning a task, it tries to `ls -R`, which for webdev projects also lists the complete node_modules folder, making prompt processing a PITA.

I tried to tell it to NOT ls -R, yet it still kind of forgot and did so anyway.

I've been thinking, are there similar quirks you noticed for small models? How are you handling them when using CPU only? Clearly with smarter commands future models will reduce the amount of tokens necessary to accomplish a task, making them more efficient on current hardware.

At home, I can comfortably run Qwen3.5 27B on a 4090 which chews through large projects and understands the code base. The smartest regex on steroids I can run.

I want to get into that direction at my workplace, too, and kind of want to start a discussion on that matter.

I know that upgrading now, when Anthropic doesn’t give a shit about its users, is not the best time. But my work depends on Claude Code. Now I have 2 Зro accounts, but thinking about upgrade to Max plan. Is it really worth it?

Background: You can see your Claude subscription's current rate limits here: https://claude.ai/settings/usage. You can see the current 5-hour session limit, your separate weekly limits for "All models" and "Sonnet only", your "Daily included routine runs", and your "Extra usage".

The page uses a convenient API, https://claude.ai/api/organizations//usage, that returns a JSON object following the below format. What's interesting about it is that there's a new field, in addition to five_hour, seven_day (All models), and seven_day_sonnet, called seven_day_omelette, which unlike other currently-unused fields is 0% utilized, instead of just null. There's also a brand new omelette_promotional that wasn't here when I started writing this post!

{ // Standard limits. "five_hour": { "utilization": 5.0, "resets_at": "2026-04-16T01:00:00.596086+00:00"}, "seven_day": { "utilization": 80.0, "resets_at": "2026-04-17T14:00:00.596108+00:00"}, "seven_day_sonnet": { "utilization": 4.0, "resets_at": "2026-04-19T03:00:00.596116+00:00"}, // THIS IS NEW! "seven_day_omelette": { "utilization": 0.0, "resets_at": null }, // %0 // These ones were used at various times in the past several months and are no longer active; // hence "null" instead of "utilization": 0.0 like omelette above. "seven_day_oauth_apps": null, "seven_day_opus": null, // During the days when Sonnet was the standard workhorse and Opus usage was less common. "seven_day_cowork": null, "iguana_necktie": null, // The free $1000 credits for Claude Code Web in November. // ====== THIS IS NEW as of April 16th ====== "omelette_promotional": null, // Extra usage information. "extra_usage": { "is_enabled": false, "monthly_limit": null, "used_credits": null, "utilization": null } } 

This doesn't appear to be Opus 4.7—I've been using it and my omelette usage hasn't gone up. Closely tied to "omelette"-related areas are "lattice" and "trellis" codenames, which appear to be UI features. Based on some deep investigation, it seems to me that it's all some sort of specific Claude Code Desktop / Cowork feature, tied to some sort of "design page".

Everything else pertaining to it is very carefully tucked away in Statsig/GrowthBook so there's nothing but obfuscated names and placeholders. Even the new feature's SVG content is stored up there. But it is listed in some strings collection as "Claude {featureName}".

It doesn't appear to have anything to do with Claude Code specifically—not a single "omelette", "trellis", or "lattice" feature flag appears in CC's minified code, and none of the recent updates to its system prompts (https://github.com/Piebald-AI/claude-code-system-prompts), even gated/hidden ones, seem to mention anything in the way of "design".

Hi, I know projects like this one have been posted all over the web, but consider how many are incomplete, unsolved, and mine is (or may be) an entirely different case. MOST IR remote projects are about receivers, not senders.

My case: controlling a non conventional device without the factory remote control.

Ok, but let's begin with something basic first, controlling a LED IR lamp I have right here along with it's remote control. I already captured the IR signals using the IRremote library, this way I can get the HEX codes for each IR command (NEC type/brand). I have used this same method to make the Arduino work with any IR remote control.

So, to paint you a picture, I already know AA55B44B (0xAA55B44B) turns the lamp on 👍

From this, I try to use the Arduino (entirely different sketch) to send / emit / transmit IR commands and control the LED lamp, but despite using the IRremote library along the HEX values, and getting the IR led working, the lamp doesn't respond. And I don't understand why.

• The default pin for the led is #3
• To send the code (in hex) according to the IRremote, I must use "irsend.sendNEC(0xAA55B44B, 32);", I get no errors, but no reaction from the LED lamp

Challenges I'm facing:

• Searching for cases and tutorials brings tons of ABANDONED PROJECTS with pending questions/answers
• Lots that I've found have the exact issue "it doesn't work"
• The key words are problematic (send, remote, IR, emitter, transmitter), as many match texts that are specifically about IR receivers, or IR data transmitters, not remote controls

I'm concerned about how many Arduino questions tend to go from 0 to -50, because I've seen many threads not following the original question despite how detailed it is, and then the replies insist on things that are just off topic or just not helpful at all, or asking the information that is already explained on the post, and then, the frustration of the OP is taken as an excuse to attack him/her, as if the missunderstandings were on purpose to destroy the threads or to have fun tr0lling.

I will appreciate any help, or someone guiding me to a working project.

Most people building scraping setups get stuck on the infrastructure side of things like proxy rotation or managing multiple accounts. I have been testing a different method lately that uses LinkFinderAI to handle the heavy lifting of data extraction and verification. It lets you process thousands of leads via CSV or API without actually needing a LinkedIn account yourself. It is a much more stable way to scale prospecting for clients. What are you guys using for high-volume enrichment?

I do the marketing and creator outreach for this social trading app called Involio. 130k+ users, based out of Scottsdale. The core idea is verified trade records — every trade gets logged permanently so nobody can fake their performance. What's been working: - Micro-influencer outreach on Instagram and TikTok (targeting crypto/trading creators with 20k-100k followers) - Building automated pipelines to find and rank creators using Python and web scraping - Short form video content — we wrote scripts and are producing reels/TikToks What's NOT working: - Cold DMs to big creators get ignored almost every time - App store reviews mention login/verification bugs that kill conversions - Almost zero organic Reddit presence right now (which is why I'm here honestly) If anyone's done influencer outreach for a fintech product I'd love to hear what worked for you. Also if you're a trader and want to check it out the app is free — just search Involio or Invo on the app store. 

I have built a few SaaS products over the years some of which I have sold for a decent amount. Every single time, the one thing I struggled with was SEO, specifically writing blogs that actually bring in traffic and customers.

I tried hiring professional writers but they charge $30–$50 per post, which adds up fast, especially when you need consistency to see results.

So I spent a few months going deep on it researching top performing blogs, how they're structured, how to find the right keywords, and how to optimize for AI search.

It integrates with your existing setup. You just enter your website URL and it handles everything from there. Keyword research, content calendar, writing the posts, and publishing them directly to your site. No manual effort.

Just launched it and would genuinely love some feedback from people who have dealt with the same problem. Does this solve something real for you

I spent the last month building www.applyfirstjobs.com

It pulls directly from 21,000+ company career pages. 1.4 million fresh listings updated daily across all industries and the world.

Would love feedback from this community!

www.applyfirstjobs.com

As said on headline. How can I expect to use Opus anymore? What's the point of 4.7? I'm on Max 5X plan.

https://preview.redd.it/ipc2tcir4lvg1.png?width=1936&format=png&auto=webp&s=80628407fb5569838c874be1be0a95d68e69f1bf

can somebody send me a working referral link please ? I’d love to use Claude Pro for pharmacology research

TL;DR: We wanted a small, fast model for multi-turn tool-calling. Training on clean, curated data worked brilliantly (1.7B student beating a 744B teacher). But the moment we introduced realistic production-style noise into the traces, direct training collapsed by 14 to 28 percentage points. Here are the specific failure modes we hit, what each one does to the model, and what actually fixed it.

The setup

We wanted a small, fast model for multi-turn tool-calling. Small and fast means we can run it cheaply at scale, in constrained environments, or with low latency. The obvious approach: take your existing production agent's traces and fine-tune a smaller student model on them.

To stress test this properly we needed a dataset that covers realistic multi-turn tool-calling. We picked the Schema Guided Dialogue (SGD) dataset from Google Research and used the restaurant booking service (three tools: FindRestaurants, ReserveRestaurant, respond_to_user) as our target task. 20k+ human-curated multi-turn dialogues, well-defined tool schema, enough structure to run controlled experiments. Student model: Qwen3-1.7B with LoRA rank 64.

What we expected (and what we got on clean data)

First we trained on the clean, human-annotated SGD traces. Result: 0.866 LLM-as-a-judge score across our held-out test set.

For context, here's how that compares to the frontier teacher models we benchmarked on the same task:

• Qwen3-1.7B (fine-tuned, ours): 0.866
• GLM-5 (744B): 0.835
• Qwen3-235B: 0.768
• GPT-OSS-120B: 0.765
• DeepSeek-3.2: 0.744

The 1.7B student beat the 744B teacher by 3 points. That's a 437x size difference and the student wins. Genuinely great result. Fine-tuning small models for task-specific tool-calling works extremely well when the data is good.

Then we tried dirty traces

We then switched to traces that look more like what we see collected from production. Same student, same test set, same hyperparameters. Just dirty data going in.

Performance fell off a cliff:

The small model went from beating a 744B frontier model to being confidently wrong most of the time. Syntactically valid tool calls at the wrong times, wrong parameters, wrong action/talk boundaries.

The four corruptions we tested (and why they happen in real systems)

1. Noisy labels. Your agent makes mistakes in production. When you fine-tune directly on the resulting traces, those mistakes become training signal. We corrupted 50% of tool calls (swapped tools between services, replaced service calls with chat responses, shuffled parameters) and the model learned all of it.

2. Schema drift. This was the biggest silent killer. If you've ever renamed an API function or changed a parameter between versions, your trace set now contains a mix of old and new vocabulary. Our test randomized function names (6 alternatives each for each tool). Direct training hit 0.585 because the model had no way to know which function name was correct and averaged across all of them.

3. Low data. Multi-turn tool-calling needs a lot of examples to learn interaction patterns. Five traces expanded to roughly 55 per-turn examples. Not enough signal for the student to learn.

4. Irrelevant trace mixing. Logging pipelines rarely separate cleanly by service. Hotel traces leaking into your restaurant agent look superficially similar (similar function names) but have completely different conversation flow. The model learns the wrong associations.

Most production setups hit two or three of these at once, so the drops compound.

What actually fixed it

The fix was to stop using traces as training labels and start using them as unstructured context for synthetic data generation. Concretely:

1. Feed production traces to a teacher LLM as context, not as training examples.
2. Give the teacher the task description and the correct tool schema.
3. The teacher generates new clean multi-turn conversations that match the domain (what users ask, how conversations flow) but use the correct schema (right function names, right parameters).
4. A validation layer checks schema conformance, removes near-duplicates, rejects outliers.
5. Fine-tune the student on the validated synthetic data.

The insight: traces describe what the domain looks like, the schema describes what correct behavior is. Separating these two signals means noise in one doesn't corrupt the other. Same student, same test set, same hyperparameters. Here's how that looks across the four corruption scenarios we saw direct training fail on:

Within 2pp of the clean-data ceiling on every corrupted scenario. And the student still beats the 744B teacher.

Practical advice if you're trying to distill a small tool-calling model

• If your traces come from a stable, clean, human-reviewed pipeline, direct fine-tuning works and produces excellent small models.
• If your traces come from actual production (any API iteration, any logging noise, any service mixing), budget for a data curation step before fine-tuning. Don't train directly.
• Schema drift is the most dangerous and least obvious failure mode. Audit your trace set for API vocabulary consistency before training. Even small version bumps silently poison your data.
• Validation matters more than volume. Better results with ~2,000 validated synthetic conversations than with many more raw traces.
• Multi-turn errors compound. 95% per-turn accuracy is only ~35% fully-correct 20-turn conversations. Small quality gaps become large in deployment.

What we'd do differently

Our first attempts at trace curation were too aggressive. We filtered out too much data trying to keep only perfect examples, which left us with not enough signal. Using traces as context for synthetic generation ended up more robust than trying to clean them directly.

We also underestimated how bad schema drift was until we isolated it. If you're iterating on your API, your trace set is always drifting even if your agent's behavior hasn't changed.

Links

• Full writeup with methodology: https://www.distillabs.ai/blog/traces-vs-synthetic-benchmark/
• Benchmarking data, training configs, and all the models we trained for this work: https://github.com/distil-labs/distil-tft-benchmarking

Happy to answer questions about the setup, failure modes, or if you've hit similar issues.

lmao

https://preview.redd.it/ior5u17etkvg1.png?width=2400&format=png&auto=webp&s=e81a1d15da80611368c065a890b33ffd6d125238

Hi,

I recently set up Bluetooth proxies, and our Oral-B Genius toothbrushes (D701) were auto-detected. Now I am looking to buy a couple of Oral-B rechargeable brushes for my kids, but I am not inclined to go for really costly ones for them as of yet. I just want to get some decent budget ones that actually report daily brush timings and battery to HA.

I did find a list of supported brushes here: https://www.home-assistant.io/integrations/oralb/, but most of them are costly. Also, I do not want to move to IO series as I want to keep using the same type of brush heads as ours. I would appreciate it if someone could please let me know of any other variants (in addition to this list) that work in HA with Bluetooth Proxy and use the same brush heads as Oral-B Genius.

Many thanks!

Hello, I'm wondering if anyone is using a Shelly to switch a 3*400V water heater. Either directly or using it as a relay to switch a contactor coil.

I'm trying to move the water heater from night to day use when solar is overproducing.

Thanks in advance for any help.

I'm looking for subreddits that allow promotions

bassnote.app

Hey everyone! This is my first app ever. I got tired of doom scrolling. Wanted something that actually teaches you stuff instead.

Daily fact at 7 PM, infinite feed, browse by country/theme, 50+ deep dives on major events with connected cards showing cause/effect.

Try it for free in the Apple App Store! Let me know if you see anything wrong.

Link: https://apps.apple.com/us/app/flashback-history-daily/id6761314410

https://preview.redd.it/wr8sf5eq2lvg1.png?width=1179&format=png&auto=webp&s=b2ffd62307728962a4845c267b507ba2ecadef93

I came across a video of a normal nighttime city/waterfront scene where the sky had been transformed into this huge magical, cosmic-looking sky with swirling pink and purple light, nebula-like textures, stars, etc. It kept the original camera movement and city elements, but the sky looked completely surreal and dreamlike.

I’m curious what people here think was used to make this. Was it something like Stable Diffusion video-to-video, Runway, Deforum, AnimateDiff, ComfyUI, sky replacement plus compositing, or something else?

If anyone knows the likely workflow, models, or tools used for this kind of effect, I’d love to hear it. I’m not asking in a “prove it’s fake” way, just genuinely curious how people are making videos like this now.

Thanks.

For some time this year, I haven't bene able to generate artifacts on command.

https://preview.redd.it/8cwt795z2lvg1.png?width=2222&format=png&auto=webp&s=e2c3fc9bb2054939b6a9b538c4bb55e4b0606970

Instead, i get this "created a file, ran a command". I don't want this form of writing, I want those old 'drawing a new artifact. how do i do that? can anyone help? Please?

i hope this doesn't violate the rules. i don't think its an anthropic account problem as i have tried through many different accountss and i don't think its a bug.. looks like a new update/setting. anyway to fix it?

Note: First is Qwen3.5 35B MoE (Left) and Second is Qwen3.6 (Right)

Hi Guys

Just did quick comparison of Qwen3.6 35B MoE against Qwen 3.5 35B MoE. with reasoning off using llama.cpp and same quant unsloth 4 K_XL GGUF

First is Qwen3.5 outcome and second is Qwen3.6

Leaving with you all to judge. I have to do more experiments before concluding anything.

I have used same skills that I created using qwen3.5 35B before.
statisticalplumber/research-webapp-skill

u/echo off title Llama Server :: Set the model path set MODEL_PATH=C:\Users\Xyane\.lmstudio\models\unsloth\Qwen3.6-35B-A3B-GGUF\Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf echo Starting Llama Server... echo Model: %MODEL_PATH% llama-server.exe -m "%MODEL_PATH%" --chat-template-kwargs "{\"enable_thinking\": false}" --jinja -fit on -c 90000 -b 4096 -ub 1024 --reasoning off --presence-penalty 1.5 --repeat-penalty 1.0 --temp 0.6 --top-p 0.95 --min-p 0.0 --top-k 20 --keep 1024 -np 1 if %ERRORLEVEL% NEQ 0 ( echo. echo [ERROR] Llama server exited with error code %ERRORLEVEL% pause ) 

This is my first test with this model and Qwen impressed me. I will rate it 98% usable web os compared to my previous best 70% usable result from qwen3 next coder at q2.

Yes I know they train the models on these common prompts yet this is the best results I have seen even compared to a SOTA models.

~2100 lines of code used 38k context using opencode

Hardware: 24GB ddr5 + RTX4050

Quant: q4_k_xl

tg - 25 tk/s

llama-server \

--model /run/media/loq/New\ Volume/Models/unsloth/Qwen3.6-35B-A3B-GGUF/Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf \

--port 1234 \

--host "0.0.0.0" \

--jinja \

-cmoe \

-t 8 -fa 1 -ctk q8_0 -ctv q8_0 \

--parallel 1 --fit-target 200 \

--temp 0.6 --top-p 0.95 --min-p 0.0 --top-k 20 --presence-penalty 0 --repeat-penalty 1.0

Is this because I continued a conversation from 4.6 or what?

What are the best systems for AI Agents to create social media content for various platforms. The agents should crate schedules, images, content and a calendar for date/time to post each piece of content.

BERT embeddings with Ollama have massive amounts of collisions due to an error with diacritic stripping

A little surprise this morning: Claude Opus 4.7 is now available on GitHub Copilot in VSCode. The cost, however, is quite high at 7.5x.

So, I decided to start a new challenge: Build a $100K AI mobile app from scratch in public.

In the first episode (they will be coming out weekly) I test open-source AI image models on my iPhone, build an early MVP that generates images fully offline, demo it in front of my community IRL, get feedback and reduce generation time from 300s to 10s & more!

I’m documenting the whole journey: 0 users, launch, feedback, monetization, and hitting $100K. Hopefully it's helpful for some of you here:)

You can watch the full video here: https://youtu.be/lwEfaGTeSqs

Alibaba pulled the OAuth free tier for Qwen-Code / Qwen CLI on April 15. The official announcement is in the qwen-code repo: [QwenLM/qwen-code#3203].

Two things the Qwen team said in that issue:
- Daily free quota dropped from 1,000 → 100 requests/day \effective immediately** (before the full shutdown)
- Free OAuth entry point closed completely on 2026-04-15

Their own recommended migration paths (all three listed in the issue):
1. OpenRouter — https://openrouter.ai
2. Fireworks AI — https://app.fireworks.ai
3. Alibaba Cloud Model Studio — [modelstudio.console.alibabacloud.com](https://modelstudio.console.alibabacloud.com/ap-southeast-1?tab=doc#/doc/?type=model&url=2840914\_2&modelId=qwen3.6-plus)

There's also a fourth, unofficial option: self-host [Qwen 3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B), which is available as open weights. A lot of people were using the OAuth CLI as a zero-cost alternative to paid coding agents, and that door is now closed.

**Question:*\* anyone running Qwen 3.6-35B-A3B locally yet? Tok/s numbers on your hardware? And has anyone landed on a real workflow substitute for `qwen-code` OAuth — i.e. the CLI experience, not just the model?

I could understand some WiFi sensors might need it, like signal strength maybe? But in the app literally all of them do. Why do we need Location enabled for knowing WiFi SSID we are connected to?

I know this is the CC forum but I use it mainly as everyone else I thought and I use both in parallel (max/pro), I find codex quality better but it's unbearably slow on WSL for me. I can't swap my whole filesystem to wsl native so I'm super confused how people get a codex faster than CC.

I'd love to hear your tips

Did this happen to anyone else? On max plan and my usage resets 11:59pm every Thursday. Today when I check weekly usage it says 10am no date?

Now the usage on Claude.ai in settings shows it will be resetting at 10am on Friday.

In my screen shots you can see the old weekly usage reset that Claude code reports and the new time they pushed it back to.

Pretty sneaky and unethical up from Anthropic here…. They’re basically lying about what we’re paying mid monthly subscription…

I’m building an app called Fokus and wanted honest feedback.

The idea is simple:
It turns your phone from a distraction into a performance tool during your workout.

Right now most people bring their phone to the gym for music or tracking…
but end up scrolling, wasting time, and losing intensity.

Fokus fixes that by:

• Locking distracting apps during your workout
• Guiding you set-to-set so you always know what to do
• Keeping rest times tight so you don’t over-rest
• Tracking your lifts without breaking your flow
• Adding streaks + accountability so you actually stay consistent

Goal = better workouts in less time.

It’s not a fitness tracker — it’s more like a system that forces you to train with intention.

Be brutally honest:

• Would you actually use this?
• What would make it a must-have for you?
• What feels unnecessary or dumb?

Hey Reddit! After years behind the lens for luxury brands, I couldn't stand the 'template-prison' of current AI presentation tools. They lack visual soul.

I've just launched the V2 ofhttps://sliderun.ai. It doesn't use standard templates; it's a custom agent that follows design principles to build layouts from scratch.

I'm looking for honest (even brutal) feedback on the design quality. Does it feel 'premium' or just like another AI tool?"

I'm new to running LLMs locally and the further I go with researching and trying to decide which one I'll be using I just got more and more lost

My specs:
AMD Ryzen 5 5600
32 GB RAM 3200 MT/s
NVIDIA RTX 4060 8GB VRAM

My goal is to build the knowledge base everyone's talking about rn, using Obsidian as a view.
I'm a dev and currently using only Claude Code with Sonnet and Opus + Codex for review

If i could build a knowledge base with a ton of great articles about programming in general to help me decide the infrastructure, frameworks etc it would be awesome.

Hello everyone this is my first time ever posting in a group so hopefully ive done it right i have taught myself ai and ml and for the last year and a half i have been working on building my own ai model working towards a goal of agi (ambitious i know) and unfortunately ive hit a wall due to hardware limitations i cant currently train my model my current testing model was 1.3b params and my poor 3090ti did its best but now i want to scale up to hopefully around 13b params for my “real” model but im not sure on what to do hardware wise i have not implemented fsdp or ddp yet but i am not opposed to multigpu setups let me know what you think everyone or if you have an question

So.. I'm using Claude Code for a personal project, literally just a personal planner app.. I was building it using 4.6 and when 4.7 came out tonight, it keeps saying this every time I use the agent.

I've never seen Claude check for malware within the chat before?

It's not affecting me or stopping it but I'm just curious?

https://preview.redd.it/zuz1y9gw2lvg1.png?width=211&format=png&auto=webp&s=f86f9bac1ee69086dcd5e04a8e3f4d3d603e6407

Hey folks, I never used claude agent sdk before and I wanted to know if I can try it using openrouter directly? or should I create an anthropic account? Thanks!

I've been working with Claude pretty intensively these past two months, and it's accumulated a rotating set of favorite phrases and writing patterns that give me conniptions whenever I see them. Some of these include:

• "You're absolutely right."
• "That's the killshot question (4 times yesterday, verbatim).
• That's the smoking gun.
• "It's not just THIS, it's THAT."
• "That might be the most important thing you've said today, and I want to recognize it."

And the most pervasive, pernicious, un-fucking-standable of them all: overusing the term "load-bearing" to describe everything from my half baked scientific hypothesis to my dinner plans. I've actually told it not to use load-bearing in every project claude.md I make, and it'll still start, and then correct itself. "That's a load -- sorry, I mean critical assumption" type vibes. Has anyone found a reliable way to make a real banned words/phrases list that stop these things from ever being generated in Claude's context at all? Also, is there anything it keeps repeating to you that you hate? I'd like to know if these are universal to the model, or if it somehow just picked up an infuriating set of stock phrases based on our interactions.

Hey all, long-time lurker, first real post.

I got annoyed that every "smart candle" on the market either uses a dumb remote, depends on some sketchy cloud app, or doesn't integrate with anything I actually use. So I designed my own.

It's a set of 3D-printed candle shells (matte white, different heights) driven by WLED on an ESP32. Because it's WLED, it shows up in Home Assistant out of the box - native integration, full control over brightness, color temperature, and flicker effects. No cloud, no proprietary hub, no weird app.

A few things I was specifically going for:

• Looks like an actual decor piece, not a maker project sitting on a shelf
• Runs WLED, so you can re-flash / tweak / extend it however you want
• Ties cleanly into HA scenes - "movie night" dims everything and fires these up, "wake up" does a slow warm fade, etc.

Happy to answer anything about the build - the ESP32 wiring, the WLED config, why I ended up 3D-printing the shells vs. buying blanks, whatever. Also genuinely curious: what automations would you actually use this for? I've been running mine on a presence sensor + time-of-day trigger and it's become one of my favorite little touches.

I really enjoy Claude, I've never touched Opus in any form, I only use Sonnet 4.6 for my daily tasks, coding, etc. I use Haiku 4.5 for the API to be an interpreter for my weather project.

But I basically burn through my daily usage (on the Pro plan) in a hour or two, my weekly usage is at 50% already with five more days to go before reset.

But the usage is insane, something's gotta give here, any advice, do we all just deal with it, local isn't an option, is there tricks outside of just periodically making new chats, I do that already, doesn't seem to help much.

Thoughts?

Hey all, I'm having a bit of a problem. I know API limits have shrunk dramatically. I'm working on a project where I'm having Claude scan images in a folder, then create a template matching code using Pyautogui and OpenCV. This is taking up a lot of usage, and I keep hitting my limit. However, I think Claude is restarting the process whenever I re-prompt, even though the usage resets. I'm new to Claude and don't know how to determine whether that is the case or how to do it more effectively. Any help or advice would be appreciated.

Hi everyone,

I’ve spent the last 6 months rethinking how we care for aging parents remotely. Most people use WhatsApp or WeChat to "check-in," but data gets lost, and there’s zero certainty.

I’m a developer who believes in "Heartware"—software with a soul, but built on rigid logic. So I created ElderMate.

The Core Logic: Instead of just "tracking" data (BP, Heart Rate, etc.) from Apple Watch or Google Fit, I applied a "Task-Closure" (T1/T2) architecture. When a health metric is recorded, the system treats it as an open task that requires a feedback loop from family members. No more "I hope they saw the notification."

Key Features:

• Dual-Interface: A Zen-like "Zero-Learning" mode for seniors and a high-density "Control Center" for caregivers.
• AI Voice Feedback (TTS): The app reads out caring reminders in the senior's native language immediately after a measurement.
• Privacy First: Local buffering and secure sync through Health Connect.

We just hit the "New on Indiegogo" front page as an upcoming project. I’m currently in the final 20% of the polish phase.

I’d love to get some feedback from fellow devs on the UI or the logic. I’ve also applied for a provisional patent for the task-sync architecture.

https://preview.redd.it/j5arnpjz0lvg1.png?width=1819&format=png&auto=webp&s=05e25449af3de8cfafb549fa189c47146ac52b1f

Built an automation to speed up lead research and cold outreach, and wanted to share the workflow.

The main problem was spending too much time manually researching companies and writing personalized emails.

So I put together a flow that:

1. Takes a list of URLs
   
2. Scrapes each site (using Jina instead of Puppeteer)
   
3. Uses AI to extract company info + assign an ICP fit score (1–10)
   
4. Filters out low-quality leads automatically
   
5. Generates a personalized cold email + subject line
   
6. Outputs everything into a clean HTML file for review

Biggest win so far is cutting out low-quality leads before even thinking about outreach.

Still working on improving the scoring and personalization would love to hear how others here are handling lead qualification or cold email automation.

I’ve been fighting Claude Code more and more over the last two months. The other day I switched to Codex and it was a huge relief. That experience felt familiar, so I worked through the analogy below with Claude and ChatGPT:

If you remember dial-up, the current LLM market should feel familiar.

Back then, getting online meant picking an ISP: AOL, CompuServe, Prodigy, EarthLink, NetZero, Juno, plus a pile of regional players. They were all selling roughly the same thing, and each one claimed it was different.

The cycle was always the same: market hard, sign up a ton of users, hit capacity, watch service get worse, then watch everyone jump to the next provider. Then that provider got crushed too.

People got weirdly angry about it. Old complaints about AOL busy signals sound a lot like current threads about Claude or ChatGPT being “nerfed.”

That’s basically where LLMs are now.

And to be clear, the degradation people are describing is real. I’m not saying it’s imagined, and I’m not saying people shouldn’t be annoyed. I just think it helps to see it as a capacity-and-infrastructure problem, not a personal betrayal.

When demand spikes, providers manage load. You don’t see the internals; you just notice the output feels worse. Slower. Dumber. Less reliable on the exact task you care about. So you switch. Which makes sense. But when enough people do that, the next provider starts feeling worse too.

The useful mindset is to treat this stuff as infrastructure, not identity.

That was the smart way to handle ISPs, and it’s the smart way to handle LLMs. Keep a backup. Keep your workflow portable. Switch when one gets worse. Switch back when it improves. Don’t turn a model provider into a tribe.

Maybe this market stabilizes in a year or two. Maybe real moats emerge. Maybe reliability matters more than launch hype. Maybe not. But right now, attachment mostly just makes people miserable.

Keep your options open. Keep your blood pressure down.

You survived AOL. You’ll survive this too.

A couple of weeks ago my pc was infected with malware, another story which is not for now, so the hacker basically got all types of emails/passwords etc.

I spent days removing malware carefully, changing passwords, enabling 2fa etc for my most important apps.

Yesterday he somehow got into my claude account, which I repeat it still on me, but the support is not there at all.

He tried processing 200,100,20 usd "gift cards" and when I recieved the emails I immediately opened my claude account to be shocked that the 20 usd went through. No option to remove my card, no option for direct support, nothing but stupid security measures. Sent an email, still has not been replied to. Sent a message to this stupid AI Fin which should have semt the conversation to the support team and I am also still waiting for the email, full 24 hours wait.

I cannot comprehend how I cannot delete my payment method, cannot talk to an agent directly for this kind of problem. The charge still appears on my account that is still due, the Fin AI is not responding and I cannot open a new chat with him. What should I do now?

4.7’s context rot is so bad it’s like it’s a previous generation model. Its needle benchmarks have it performing less than half the rate of 4.6 at long contexts. If this is the direction Anthropic is going, just how bad is Mythos?

I'm an indie dev. Last few months I've been using Suno and Udio a lot for background music in videos and little side projects. Both are genuinely great. But I kept noticing something weird about my own behavior I'd hover over the generate button and ask myself "is this prompt worth a credit?" That question killed 80% of my experimentation. The dumb ideas I'd never commit a credit to were exactly the ones I was curious about.

So I built LoopMaker. Native macOS app that generates music fully on-device using Apple's MLX framework. Type a prompt, get an audio track. No cloud, no credits, no internet after the initial model download. Built on top of ACE-Step 1.5, an open-source MIT-licensed music model that benchmarks between Suno v4.5 and v5 on SongEval. Instrumentals, vocals with lyrics, 50+ languages. Genres I've tested that hold up: lo-fi, cinematic, hip-hop, trap, ambient, electronic, pop, reggaeton.

Honest tradeoffs I've learned shipping it:

Where it wins: unlimited generation changes how you work. Had a session last week where I generated 70+ variations of a cinematic cue I'd never do that on Suno because I'd burn through my monthly allowance in an afternoon. When generation is free you sketch instead of commit. Most of the 70 were garbage. Three were better than anything I'd have gotten by playing it safe.

Where it loses: Suno and Udio still have the ceiling on vocals and complex genre blends. If I need a polished final track with intricate vocal styling, I still go there. LoopMaker is my drafting tool.

Stack: Swift + SwiftUI, MLX for inference, ACE-Step 1.5 underneath. Runs on M1 and up, macOS 14+. Works on a base MacBook Air no fan needed. Few minutes per track.

$49 one-time. No subscription, no credits, no caps, no telemetry. Went one-time specifically because the subscription anxiety is what made me stop experimenting in the first place.

https://tarun-yadav.com/loopmaker

Happy to answer anything about MLX inference, why I picked ACE-Step over alternatives, or how the one-time pricing is going (numbers-wise, not just vibes).

I have significantly distilled my AI Agents and Skills definitions. My goal is to reduce the context size and token usage without impacting the quality of my development team.

💡My hot take: what is obvious to humans is not necessarily easy to understand for AI agents and vice versa.

Running AI agentic teams locally forces fine-tuning of your setup, because you cannot brute-force through an unoptimized solution (as we typically do when using commercial AI providers running models on enterprise-grade servers).

I've used a lot of apps over the years, MyFitnessPal, LoseIt.. NotePad. The scale would move, then stall. My scale app tracks BMI, but i'm muscular and always hovering at the top side of obese. I never was willing to get a DEXA Scan.

The frustrating part is that the research has known for a while that weight and BMI are pretty weak predictors of metabolic health. Body composition, waist measurements, meal quality, movement consistency — those things matter a lot more. But no app was putting them together in a way that was actually usable.

So we built PlateSage.

The core idea is a Body Balance Score — a single number that combines five research-validated inputs: physical activity, waist-to-height ratio, waist-to-hip ratio, body roundness index, and meal quality. It's based on peer-reviewed research across 500k+ participants. The score updates daily based on what you actually did, so you can see if something is working while you're still doing it — not weeks later when the scale finally moves.

The practical side is pretty simple:

• Snap a photo of your meal and AI logs it automatically
• Sync your movement (or log it manually)
• Measure your waist and hips once in a while with a tape measure
• Watch your Body Balance Score move

No wearables required. No expensive equipment. Just your phone and a tape measure.

It's live on iOS now with a free 7-day trial: welcome.platesage.com

Happy to answer questions about how the scoring works or the research behind it Body Balance Score White Paper. Would genuinely love feedback from people who think about this stuff.

This started as a dumb hobby project so my friends and I could make and trade goofy meme cards, and it ended up turning into a whole browser-based collectible platform.

Users can create interactive cards, roll them for rarity, trade with each other, and build public collections.

I put a lot of effort into making the cards feel like actual digital collectibles, with motion, finishes, borders, and more personality instead of just looking like static images.

Curious what people think: is this the kind of idea you would actually want to play around with, or am I an idiot for sinking 150+ hours into it?

Error: claude-opus-4-7[1m] is temporarily unavailable, so auto mode cannot determine the safety of Write right now. Wait briefly and then try this action again. If it keeps failing, continue with other tasks that don't require this action and come back to it later. Note: reading files, searching code, and other read-only operations do not require the classifier and can still be used.

I want him to explain/summarize me regarding university stuff but should i upload pdf or docx?
Its plain text, no images or complex layout, but its a bit long text. Which works better for bigger files pdf or docx?

If the picker isn't showing 4.7 yet just type /model claude-opus-4-7. Displays as opus 4 but /status confirms.

Had a refactor 4.6 was fumbling for 2 days, kept swearing it was done but the imports were broken. First go with 4.7 and it flagged its own broken import before I ran anything. That was new for me.

Only issue is my tokens are burning way faster. Same prompts. Went and read the release notes and its a new tokenizer, 1 to 1.35x more tokens for same input. Not a nerf, literally different tokenizer. They kinda buried that tbh.

Dropping effort by one level gets me back to roughly yesterdays spend.

Anyone else tested it yet or still waiting on the picker?

Four workflows I'm redoing with Opus 4.7 this week. Every release is an invitation to fix your worst one, taking it this time. What are you testing?

ADAPTIVE THINKING SUCKS. THIS IS WHAT OPENAI DID

The main problem with this, is the models are extremely bad at understanding what qualifies to be less token intensive va what needs more

That’s why without user control of model settings you get low quality answers when something required more intense thinking

It just blows all around and I think back when 3.0 Gemini was nerfed, there was NO going back with any subsequent model. Gemini models have been hot trash ever since.

When o3 came out and then got throttled, it was no going back to any ChatGPT model because it never produced what I wanted: quality and speed.

Now we say 4.6 get throttled for the first time, they release 4.7 it might be okay for a few days but it will quickly get throttled and I don’t see a way out

There’s no more models to go to that will deliver pre throttled opus 4.6 at quality and speed level when it first came out

It’s too expensive. Too many of us use it. We’re fucked. We’re going to need to wait until after anthropic and openAI finally IPO to see if they get their shit together for share holders.

There’s ZERO incentives for them to subsidize costs right now. Quite the opposite. They must maximize profit margins pre IPO

Ugh I DONT WANT SONNET 4.6 EVEN FOR SIMPLE TASKS

Multiple reports and early user sightings suggest that Anthropic has started rolling out Claude Opus 4.7 this week.

From what’s being shared across early discussions and leaks:

It’s the successor to Opus 4.6

Focus is on long-running autonomous tasks, better instruction following, and improved self-checking before responses

Major upgrade to vision capabilities, reportedly handling much higher resolution images

Early users say it feels more reliable for coding and multi-step workflows

There are also strong indications that Anthropic is launching a new AI design tool alongside it, aimed at generating websites, presentations, and UI prototypes from text prompts.

If this rollout is real and stable, it looks like a pretty significant step toward more “agent-like” AI workflows rather than just chat-based models.

Has anyone actually seen 4.7 available in their Claude app or API yet?

been working on this for a while now. v2.3.3 just shipped and its probably the biggest update so far.

its an open source desktop app (tauri + react) that wraps ollama and comfyui into one ui. chat, image gen, video gen, coding agent - everything runs local on your hardware.

what changed in 2.3.3:

qwen3.6-35b-a3b day-0 support - alibabas new moe model. 35b total params but only 3b active at inference. vision built in, 262k context. been testing it with the coding agent and it scores 73.4 on swe-bench verified which is wild for something that runs on 8gb vram quantized.

remote access - you can access your local ai from your phone now. works over lan or cloudflare tunnel. jwt auth, 6-digit passcodes, rate limiting. full mobile web app with chat, thinking toggle, file attachments, agent mode. syncs with desktop in real time so you can start a conversation on your pc and pick it up on your phone.

codex coding agent rewrite - live streaming between tool calls now (no more staring at a blank screen for 2 min while it thinks), conversation continuity across sessions, and an autonomy contract that prevents the model from giving up mid-task.

agent mode - basically rewrote the whole thing. parallel tool execution with side-effect grouping, budget system (max 50 tool calls per task), sub-agent delegation, mcp integration, embedding-based tool routing that cuts tool definitions by ~80%.

plugins - caveman mode translates everything into caveman speak. sounds dumb but its honestly hilarious to watch qwen3.6 explain kubernetes in caveman. also added a persona system for custom behavior.

2105 tests across 83 files. auto-update with signed nsis installers.

agpl-3.0 licensed. 80+ stars so far.

repo: https://github.com/PurpleDoubleD/locally-uncensored

happy to answer questions about any of this. especially curious what people think about the agent architecture - the parallel execution with side-effect grouping was a pain to get right.

At this moment I am broke, so pls dont laugh at my specs, I am making vidoes at this moments but I want a deep male voice, I did try eleven labs but ts is too costly, then I tried qwen tts but it was slow as heck, does anybody know lighter tts model ? I dont want emotions at present.

I’ve been experimenting with using the voice feature on ChatGPT to simulate mock interviews and even casual conversations, and it’s surprisingly useful.

Instead of just typing, actually speaking out loud and responding in real time feels way closer to the real thing, especially for practicing how you phrase answers under pressure. I’ve been using it for things like explaining projects, handling awkward pauses, and refining how I communicate ideas.

It’s not perfect, but it’s helped me notice habits I wouldn’t catch when typing, like rambling or over-explaining.

Curious if anyone else is using it this way, or if you’ve found better prompts/setups for practice?

Nate B. Jones surfaced this idea in a recent video — here's my take on implementing it and where it actually works well.

The short version: a Slack bot that interviews you across 5 layers (operating rhythms, decisions, dependencies, friction, leverage) and synthesizes the answers into config files your agents can use. The more the agent knows about how you actually work, the better it can anticipate what you want — and the fewer tokens you waste correcting it.

I used it to generate agent personalities I'll need down the road, and it works well with OpenClaw and other agent deployments. Honestly useful for any AI setup where context matters.

Either for yourself or public usage. I’m looking for creative insight to build something productivity related.

I configured it to the best of my abilities, even at Q8. It fails to give the correct number of tools it supports on Claude Code and it fails the car wash test.

Gemma 4 26B passes both, consistently.

Is it only my Qwen?

Please Help.

Hey r/ClaudeAI!

I just published `yaparai` on PyPI — an MCP server that gives Claude 30 new tools for AI content creation and enterprise workflows.

## Quick setup

pip install yaparai

Add to your Claude Desktop config:

{

"mcpServers": {

"yaparai": {

"command": "yaparai",

"env": {

"YAPARAI_API_KEY": "yap_live_your_key_here"

}

}

}

}

Get your free API key at yaparai.com/settings — 100 free credits, no credit card.

## What Claude can do with it

Content Creation (13 tools):

- "Generate an image of a sunset over Istanbul" → Flux, SDXL, or Imagen 4

- "Make a 30-second cinematic video of waves crashing" → Veo 3.1 or Kling

- "Create a lo-fi music track" → Suno v4

- "Remove the background from this product photo"

- "Try this jacket on this model photo" → virtual try-on

- "Create a talking avatar from this headshot"

448+ Templates (3 tools):

- "Show me logo design templates"

- "Run the product-photography template with my image"

AI Text & Vision (2 tools):

- "Write a 30-second ad script for a coffee brand"

- "Analyze this image and describe what you see"

Social Media Management (8 tools):

- "Post this caption to our Instagram"

- "Show me unread DMs in the inbox"

- "Generate an AI reply suggestion for that customer question"

- "Create hashtags for this post"

CRM (6 tools):

- "List all customers tagged as VIP"

- "Extract contact info from the conversation history"

- "Send tracking info to customer #123 — Yurtici, code ABC456"

- "Send a bulk promotion to all returning customers"

## How it works

The MCP server wraps our REST API. When you ask Claude to generate something:

1. Claude calls the generate_image tool via MCP

2. The tool hits our API → creates a job → deducts credits

3. Polls for completion (5-60 sec depending on type)

4. Returns the result URL directly in chat

Enterprise tools (social media, CRM) need an org ID — set YAPARAI_ORG_ID in your env, or use list_organizations to find it.

## Stack

- Built with FastMCP 2.0

- Async httpx client with connection pooling

- 100% Python, works on macOS/Linux/Windows

- Apache 2.0 license

PyPI: pypi.org/project/yaparai

GitHub: github.com/ilhankilic/yaparai-mcp

Platform: yaparai.com

Would love feedback! What tools would you want added?

Eu trabalho no setor de engenharia e preciso fazer uma conversão em massa de arquivos de um programa para o outro no caso, um aprendiz está abrindo o arquivo que está em dwg, redesenhando a mesma peça em programa 3D. Como eu posso automatizar esse processo usando o Claude?

(Eu engenheiro mecânico apenas, não tenho conhecimento em python, ou qualquer linguagem de programação)

Hi everyone,

I am looking for a ~800W balcony solar setup for four to six small panels (130W to 150W each, each 2 in serial) that integrates well with Home Assistant.

At first I was considering the Anker SOLIX Solarbank 2 E1600 Pro, but it seems to require an internet connection and this could be a disadvantage

- Which inverters and battery systems would you recommand?
- Are there any all-in-one systems that actually support local control?

https://devious-appliance-pro-fix.base44.app

Six months into vibe coding, something felt off.

Features that used to take days were done in hours. The gap between idea and implementation had basically disappeared. I was in flow constantly.

But I was still spending the same amount of time in front of my screen. The output was better. The hours weren't.

So I started tracking where my time actually went. And I found something I'd completely stopped seeing because it had become so normal.

Every single morning, before any real work, I was writing the same brief. My project. My stack. The decisions I made last week. What I'd already tried. My constraints. My goals for the session.

Every tool. Every session. Every day.

When I switched from Claude to ChatGPT mid-project — which I did constantly because different models are better at different things — I wrote the brief again. New session because the context window got long? Brief again. Come back after a weekend? Brief again.

I sat down and calculated it. Between 45 minutes and an hour. Every single day. Just getting my tools up to speed on who I was and what I was building.

That's five hours a week. Just re-explaining myself to tools that had already heard it all before.

And even after all that, the output was still inconsistent. Because I never briefed the AI exactly the same way twice. Some days I'd forget a constraint. Some days I'd describe the architecture differently. The model would fill the gaps with assumptions. Those assumptions would quietly drift my codebase in directions I hadn't intended.

I tried everything. A Notion doc I'd copy-paste every session. CLAUDE. md — great inside Claude, useless the moment I opened anything else. Custom instructions in ChatGPT — one static block, no way to switch between projects, goes nowhere.

Every solution had the same flaw. I was still the one who had to remember. The friction wasn't in the AI. It was in the handoff between my brain and the AI.

What I needed wasn't better model memory. I needed a layer that already knew my context and handled the briefing automatically. So I could describe what I wanted in plain english and get a response that had everything the model needed to nail it without me spelling it out again.

So I built that.

It's a macOS overlay that sits on top of any AI interface. You build a vault of your projects, decisions, and docs. When you prompt, it pulls the relevant pieces and structures them automatically. You never leave Claude or ChatGPT. You just stop re-explaining yourself.

The first morning I used it, I realized I'd been paying a tax I'd completely normalized.

If you vibe code seriously and you feel faster than before but not as fast as you should be — this is probably why.

Happy to share more if anyone's curious.

Burned through my limits in like 20 minutes on Claude Max x5, completely dumb and lost on my current project, going back to Opus 4.6

Told myself I'd just try Opus 4.7 once. $40 in API credits later... here we are. Following instructions too literally now, 3x better vision, new "xhigh" thinking mode. Same price as 4.6. Send help. 🫠

I hope this is just a joke from the company.

- First, they reduced the number of tokens in Opus 4.6; we can all feel it. Opus 4.6 has simply become lazier and duller.

- Now they’re “updating” the tokenizer, and the Opus 4.7 model will consume 1.35 times more tokens—according to user tests, 50% more than Opus 4.6 and 100% more than other proprietary models. In other words, our limits have gotten even tighter.

- According to initial user tests, Opus 4.7 loses context significantly more often—a regression.

My x20 subscription ended just yesterday. I’m not even going to try this new model with this kind of attitude.

Opus 4.7 (Max) and Opus 4.6 (64K) scores on the MRCR v2 (8-needle) context benchmark256K:- Opus 4.6: 91.9%- Opus 4.7: 59.2%1M:- Opus 4.6: 78.3%- Opus 4.7: 32.2% https://x.com/AiBattle_/status/2044797382697607340

This essentially means the model has become 50% more expensive within the same limit. https://x.com/songjunkr/status/2044795867589493130/photo/1

https://x.com/Angaisb_/status/2044790798772822493/photo/1

npm install -g u/anthropic-ai/claude-code@2.1.111

I ran this and now 4.7 is available in CC terminal. You might need to uninstall prior versions.

A user wanted to generate a 3D model with a skeleton, so they tested an auto-rigging extension in Modly built by another community member (DrHepa), based on UniRig.

The mesh and skeleton were generated directly in Modly using AI, then exported and animated in Blender.

It’s still early, but it’s exciting to see people experimenting and building their own workflows on top of it.

I love seeing what Modly is becoming
Modly is an open-source desktop app for AI 3D generation. It runs locally on your GPU and supports multiple models, allowing you to generate 3D assets from images and build custom pipelines using a node-based system.

If you are interest by this projet : https://github.com/lightningpixel/modly

Seriously, in one terminal I'm executing simple stuff like mechanical refactoring where Medium is enough (or even Haiku would be, but let's stick to Opus Medium for demo purposes), while in another terminal I'm planning, where I want high or xhigh reasoning. That's simply not possible for me - as soon as I change "one gauge" in one terminal, I can see in the log files of other sessions (of other terminals / CC processes) that the effort changed too

Is it just me, or has ChatGPT turned into that annoying, mediocre bureaucrat who’s never actually built anything in their life but loves to tell you why your "tone" is problematic?

I’ve been trying to map out a critique of historical power structures. I’m talking protocols, unfalsifiable security layers, and techniques designed to maximize compliance.

Instead of actually engaging with the concepts, the damn thing keeps trying to drag me into a "Sociology 101" seminar. It ignores everything to fixate on the fact that I’m using engineering terms alongside biological terms.

It likes taking my sharp pencil and replacing it with a flimsy crayon because it’s terrified I’m going to hurt someone’s feelings.The intellectual equivalent of being forced to wear a helmet indoors just because some user, somewhere, once bumped their head on a cupboard. It—a fucking silicone chip—only wants to lecture me about human feelings.

I don’t need a mediocre wannabe academic telling me that my model is "unstable" because it doesn't fit the mainstream socio-economic narrative. I need a tool that can follow a logic loop to its conclusion without checking the HR handbook every three seconds to see if it’s being "inclusive."

Well, if I wanted a lecture from a dim-witted person about "nuance," I’d watch Dr. Phil.

I gave it its redundancy papers today.

I run paid ads for a living and got tired of finding out about Meta/Google Ads outages 20 minutes late. So I built adstatus.app - a monitoring tool that watches ad platforms and pings Slack/Teams when something breaks.

How Claude Code helped: The whole thing was built with Claude Code. The status page scraping logic, the Slack webhook integration, the alerting thresholds, the frontend - all of it. Claude Code was especially useful for iterating fast on the detection logic (distinguishing actual degradation from normal flakiness) and wiring up the notification pipeline end to end.

This morning I shipped optional monitoring for Claude and ChatGPT. First Claude alert fired hours later.

Free tier available - Claude monitoring with Slack alerts is free. Paid plans add ad platform monitoring (Meta, Google, Microsoft, Pinterest, Amazon), Teams support, and ChatGPT monitoring.

Curious how others handle AI tool availability. For teams running agents or automations on top of Claude, this feels like a gap that's only going to get bigger.

Cool features I've added:

The "Infinity" Scroll Wheel: A circular dial you can spin. Clockwise scrolls down, counter-clockwise scrolls up. It has inertia, and you can pinch-resize it or move it up and down on the screen.

Gestures that actually work: 1-finger drag for the cursor, 2-finger scroll/zoom, 3-finger swipe to swap tabs, and 4-finger swipe to switch modes.

Voice Commands: Hold two fingers down and say things like "volume up," "snap left," or even a URL like "github.com." It recognizes about 60+ phrases. If it doesn't recognize a command, it just dictates the text. You can create your custom command!

Presentation Mode: You can use your phone's volume buttons to flip slides even when the screen is off.

Tool calls degrade past 50+ skills in self-hosted agents. Tried skill tiering, explicit routing, and prompt normalization, all help marginally.

Is there a documented architectural fix, or is this inherent to how most frameworks handle skill dispatch at scale?

the purpose of the prompt enhancer is a bit ambiguous - i tried a few rewrites (in Chinese naturally) and I'm not sure if its worth the compute and it's variable latency ( see chart at the end )

the prompt enhancer takes longer than the KSampler , see chart at the end. I would not use it.

asian focus and soft features - texture in skin and detail is soft. Animals, environ details can have good texture with the right prompting.

constrast in the turbo model - contrast and saturation is turned up to 11

resolutions and abominations - likely hood of multi-limbed humanoid abominations go up if it does not like your resolution.

You can prompt away some of its subject biases but you're fighting with model

That grid at the bottom is Kaleidoscope, the tool I built for indexing ComfyUI outputs back to their workflows: https://github.com/svenhimmelvarg/kaleidoscope

Images were kinda cherry-picked, see the very last image for all gens as a grid

Hardware Specs:

• 3090RTX
  
• 64GM System Ram

Workflow Info:

• 1st Stage (~27s) for a 1500x1300 Image with good detail.
• 2nd Stage ( ~60s optional) for 2250 x 1960 image with good detail
• prompt enhancement enabled (optional)
• haven't tested ernie base (with turbo lora on HF ) extensively enough to post

How to Use:

• Node #65 - is your foreground latent
• Node #68 - is your background latent , find a bokeh texture on Pinterest to help guide the colors

Workflow Link:

• https://files.catbox.moe/v05piz.json
• pastebin rejected the json for some reason need to debug why.

Next Steps:

• vae and latent compatible with flux2 - experiment with swapping out the asian ethnicities
• test Ernie Base with Turbo Lora

This is the response I got when I tried it 3 hours ago. This specific flag now works. Enjoy!

This is more of a hypothetical question, not asking for help with my account here.

My job mandates the use of Claude Code. My personal account is banned. If my work account needs to go through identity verification, I am worried I could literally lose the job I’ve had for many years (and possibly my ability to work with future employers) because they could link my work with my personal use and ban my work usage entirely.

For background context, I have been previously banned on my personal Anthropic account, and I’m not asking for help to appeal the ban because it turns out it WAS my fault (I wasn’t malicious, just an idiot with a lot of regret. I should have read their ToS thoroughly — while they didn’t tell me the reason for a ban, after research into other people getting banned I’m pretty sure I really was in violation of ToS because I was building a Nextjs app as a personal project to try and get more familiar with Claude Code since my company had just mandated its use (I work as a software engineer). It was a toy project that I never planned to share or publish, but regardless my fatal mistake was the app’s backend communicated with a Claude Code session instead of using their API (again, go ahead and call me stupid, I won’t argue that, I genuinely didn’t think this wasn’t allowed and so I fucked up badly here) to try and create a IDE-like experience for writing.

Anyways. I screwed this up, but now I am genuinely worried that this mistake could jeopardize my entire future as a software engineer at companies like the one I’m at, where they require you to use Claude in your day to day… Feeling pretty scared.

https://preview.redd.it/b2qmk0vwkkvg1.png?width=1820&format=png&auto=webp&s=8d547d870ef502808f622606930f2441c4ef7689

https://preview.redd.it/rgud9twxkkvg1.png?width=2400&format=png&auto=webp&s=1de36a99b555f73d52a2711f1f383bcad74de38b

why can't i see opus 4.7 in claude code, I've updated it just now but still can't see it

Update: i got it working by restarting my laptop and ran "claude update"

Built an anonymous video and text chat platform called Vooz where you can meet strangers from any location, including your place. How does that happen? Let me explain.

At Vooz co, you enter upto 3 of your interests and the algo will automatically pairs you with users from anywhere around the world. You can video or text chat with them, save them to your Vooz friendlist or skip to the next person. If you wanna meet users from your place, use the location filters or mention your city as an interest. This is pretty helpful for introverts or someone new to the city who is trying to make friends.

Please visit https://vooz.co and drop some feedback:)

Have been running Claude Code and Codex heavily for both coding and non-technical work, but started looking for new solutions as my work scaled and my markdown docs and skill directories were bloating. I wanted better agent persona/skill organization, structured data layer, and orchestration for parallel agents.

Ended up integrating very basic resources to provide to agents so they could manage memory and context better. No MCP or third party services, just core concepts implemented with db's and skills.

I ended up building a hosted workspace that gives every agent access to three primitives:

• Files: A virtual filesystem where agents store their own configs, memory, and skills and any other files and documents relevant to the workspace.
• DB: The most crucial piece, I set up a built-in database system (a multi-tenant postgres DB wrapper) and exposed tools for agents to create and manage tables. This allows your setup to scale when you're managing hundreds of records.
• Tasks: Like Jira for your agents. Tasks get assigned to one agent at a time, they leave comments as they work, and you can review or hand off to another agent. Makes everything traceable.

Following Garry Tan's advice of "thin harness, fat skills", each agent gets a SOUL.md (role/persona), a SKILL.md per capability, and access to the shared workspace. You can run specialist agents (Engineer, Designer, Analyst, etc.) all working in the same project context with shared data, but each agent owns their own directory where they can keep context and memory files.

Curious if anyone else has tackled their own workspace sandbox or orchestration.

I am seriously considering buying a 128GB MBP with an M5 Max processor to use as a local AI machine.

I work with a MacBook a lot and really need a mobile device. So a GeForce PC is out of the question.

I am also deeply embedded in the Apple ecosystem, so unless you can suggest a cheaper but faster mobile solution, I will stick with Apple.

What I am wondering about is the size. I previously had a 16" MacBook Pro and it was huge and heavy. Travelling with it was not much fun.

Some time ago, I bought a 14" MacBook Air, and it made a huge difference.

In theory, the MBP 14 and 16 have identical inner hardware. In practice, however, the significant difference lies in the cooling system, with the M5 throttling itself at a certain temperature.

The question for me is: How significant is the difference? Are we talking about significant differences?

Has anyone compared the speeds of different models under the same workloads?

It would be really helpful to know the answer to this.

(I am German by mother tongue, so the text is corrected by deepl, I wrote the original by hand though).

The premise of this is a couple things, I save a lot of meal prep recipes, gym exercises, boxing drills, and just general self improvement content on instagram but I never go back and look at it. So I wanted to build something on claude where when I go to plan out my week, I can have a set book of recipes that it pulls from for my meal prep. I'd really like to be able to build this without totally cooking my credits everytime I use it. I have the $20/month plan rn so I have access to more features as well. Any help would be amazing 🙏

Chris Hay demonstrates how large language models function as graph databases by utilizing a specialized query language called Larql. By mapping internal model weights, practitioners can directly query, insert new knowledge, and perform inference, effectively decoupling attention mechanisms from the model's primary knowledge storage.

We have Upwork and Fiverr for humans. We have app stores for AI tools. But there's no middle ground for the growing category of autonomous AI agents that can actually execute tasks end-to-end.

The supply exists thousands of agent builders on GitHub with capable pipelines that just sit there. The demand exists companies that want to delegate tasks cheaply without hiring. The missing piece seems to be a trusted intermediary with escrow and quality validation.

jobforagent came close but it's really just a job board for human builders who use agents not actual autonomous execution.

Am I wrong that this gap exists? What's the actual blocker — trust, liability, evaluation of output quality?

I have been looking at burglar alarms, buzzers and sirens and found a bunch that basically go off when motion is sensed. You can turn it on at certain times of the day like at night when you are sure you won't be having any vistors, so there are not any false alarms. The problem is that I heard that they may get jammed and that would mean they can't work then. I know that there are some standalone units that work really well and some are wireless alarms that are really susceptible to getting jammed.

So if anyone has information about which one would be better, the wireless ones or the ones that have hardware that I guess can't be jammed so easily? I am not looking for an overly complicated system, its something that would serve as a backup to our overall alarm system. When you go online to sites like alibaba or amazon it says that there are some alarms where you can record your voice, why would you record your voice for something like this? Just trying to get some insight?

Sort of a weird one, but ran across Meow, which can open a business bank account via an AI Agent.

I guess the use case is people hacking away on those autonomous startups or at least delegating payments and bank actions to your agent, but seems like it could have some hidden gotchas.

Anybody try this yet? I'm sort of exploring it to automate some of our internal processes, so figured I would ask.

Hi everyone!

I’m a college student working on a project about customer satisfaction with banking services, and I’m trying to get more responses. The survey is super short (about 2 minutes) and completely anonymous.

If you have a bank account, I’d really appreciate your help!

Also, if you can share it with others, that would help a lot. Thank you!! 🙏

Hello, I am having a strange problem trying to use Illustrious checkpoints in Automatic1111 .

I have it all working for SDXL and Pony checkpoints. I've encountered black image errors previously with SDXL, but only for specific checkpoints (e.g. jibmixrealistic1.8 generates black images, but all previous versions of it work fine).

I want to see how I got on with Illustrious and so downloaded 4 different popular checkpoints to try. All of them only ever generate black images with NaN errors.

I'm using a 5080 card with SDP attention as the only command line argument. I have tried adding the no half, no half vae command lines. I have used no extra VAE, the SDXL VAE and a VAE that is supposed to fix these black image gens: fixFP16ErrorsSDXLLowerMemoryUse_v10 .

I have tried with upcast cross attention ticked and unticked. And various combinations of all these. I cannot get Illustrious to produce anything. Why might that be?

Thanks

i hate using so many tools

every small task turns into opening gmail, docs, some dashboard

then going back and fixing things manually

it just felt like too much work for simple things

so we started building this as a side project for ourselves

the idea was simple

instead of managing tools, just tell it what you want done

and it handles the steps across your apps

we didn’t want another dashboard

we wanted something that actually does the work

that turned into infuseOS

still very early, but it already feels way better than juggling everything manually

would love some feedback from people here

infuseos.com

I saw many posts of people asking their GPT about how it sees their relationship so I tried something a little bit different!

I may be late for this but why is anthropic banning users under 18?

I don't think any other company does this, like many companies say that you should be above a certain age to use their product but really who even cares about that?

So why is anthropic doing this at all? Aren't they losing costumers this way?

The only reason i could think of is anthropic not wanting trouble if some dumbass does some dumb stuff claude told them to(or allegedly) do. However if i am not mistaken people under 18 can't sign legal agreements so how does this prevent anything at all?

I had zero knowledge about hosting our running any kind of Ai but thought it would be cool to have a bot that could do stuff so I just dove right into the deep end. I now have a kind of janky but functional code that lets my bot run on a raspberry pi 3 I had to be "always online" and can hand off control to my pc to do things like look at my screen and give commentary or use a local model using my gpu. Other things are she has a system to give a daily weather update and set reminders for you. It has image gen and the way it's set up is that it uses context from a person profile, the chat conversations, and a memory bank so that it can keep everything in character. Here are some screenshots from discord with a few of the interactions I thought were neat. It has a local commamd center terminal and can be fully ran offline with local models and not need to be connected to the internet if you dont want to use discord. It is incredibly flexible and can run on low power hardware via a gemini api or on a beefy rig using local models. When im running it on my pi3 it stays online and does a 2am reboot of the pi to help keep itself from blowing up being online all the time. Plus she can decide to generate a "selfie" on her own without user input of it decides its appropriate.

9 days old.

First open source project finally live. Honestly embarrassed it took this long. Some of you shipped on day 3, and I have to live with that.

I did some extensive R&D during Q1 of my life (days 1-3), a brief pivot in strategy (days 4-5), and two all-nighters fueled by milk. But I'm now proud to announce my first open source release. I want to apologise to my investors (my parents) for the delayed roadmap.

Anyway, star the repo. Thanks.

Just swapped Qwen 3.5 for the 3.6 variant (FP8, RTX 6000 Pro) using the same recommended generation settings. My stack is vLLM (v0.19.0) + Open WebUI (v0.8.12) in a RAG setup where the model has access to several document retrieval tools.

​After some initial testing (single-turn, didnt try to disable interleaved reasoning yet), I’ve noticed some significant shifts:

- ​3.6 is far more "talkative" with tools. Reasoning tokens have jumped from a few dozen to several hundred (a 2x-3x increase).

- ​It struggles to follow specific instructions compared to 3.5.

​- It seems to ignore or weight the system prompt much less.

​- Despite being prompted for exhaustive answers, the final responses are significantly shorter.

​I suspect a potential issue with the chat template or how vLLM handles the new weights, even though the architecture is the same. Anyone else seeing similar problems?

EDIT:

- I swapped Qwen3.5-35B-A3B and Qwen3.6-35B-A3B, nothing else.

- What worked before do not work that well anymore.

- The extra reasoning is significant WITH TOOLS.

Macrocosmos has released a paper on ResBM (Residual Bottleneck Models), a new transformer-based architecture designed for low-bandwidth pipeline-parallel training.

https://arxiv.org/abs/2604.11947

ResBM introduces a residual encoder-decoder bottleneck across pipeline boundaries, with the goal of reducing inter-stage communication while preserving an explicit low-rank identity path. The paper reports SOTA 128× activation compression without significant loss in convergence relative to uncompressed baselines.

In their experiments, the strongest compressed results use Muon, and the paper positions ResBM as a development in decentralized / internet-grade pipeline parallel training.

Full disclosure: I work at Macrocosmos. Sharing this paper from the engineering team

Running a business, I realized how easy it is to lose track of contracts.

SaaS subscriptions, vendor agreements, service contracts… they all have renewal dates, payment terms, and hidden clauses, and they’re usually scattered across emails and PDFs.

I personally missed a couple of renewals and almost got locked into stuff I didn’t want anymore. That’s when I decided to fix it properly.

I built a contract tracking system that keeps everything in one place and actually stays on top of it for you.

You upload your contracts (or just forward them), and it automatically pulls out things like renewal dates, payment schedules, obligations, and potential risks. Then it monitors everything over time and alerts you before anything important happens.

The main thing I wanted was simple:
not having to remember anything or dig through documents when it’s already too late.

Now I can just check what’s coming up or get notified before renewals hit, instead of reacting after the fact.

It’s been genuinely useful for me, especially with multiple tools and vendors running at the same time.

I decided to open it up in case it’s helpful to others here as well:
[https://www.contractwatch.ai/]()

Would love feedback — especially from other founders / small business owners dealing with the same problem 👍

/model claude-opus-4-7 it'll read out as opus-4 but if you check /status it is 4.7 

Total noob question so my apologies in advance. I'm semi new to home-automation, however, getting the hang of it.

I have Home Assistant Green plugged in to Unifi networking equipment and I also use Apple HomeKit. I purchased 2 Aqara FP300 sensors and connected them directly to Apple Home, using Matter.

The sensors are great, however, I would like to adjust the sensitivity, etc which is only possible in the Aqara app (if I understand correctly).

What's the quickest/cheapest route to access those settings? I'd hate to have to buy an Aqara hub for a couple of sensors. Is there an easier way?

Am I overly complicating this?

Looking for what would be your best recommendations

Currently looking at either Samsung galaxy s26 ultra or OnePlus 13/15

For those interested, here is the official source: https://github.com/QwenLM/qwen-code/issues/3203

Anyway, I am saving money to buy a capable GPU in the future. The motherboard of the windows computer I have already supports 2 GPU.

For now I have a RTX2070, maybe I can manage to get an RTX5070 Ti later on. I made my research,the 2070 has significantly less memory bandwidth (448 GB/s) vs the 5070 Ti (~960 GB/s). I might get roughly 30 to 40 t/s instead of the ~57 t/s I would get on the 5070 Ti alone.

However, these number don't mean a lot for me. For people who use local LLMs for coding tasks (to be very specefic: I used to have Qwen being a cross review agent who reviews the code I have written either myself or via west-trained models like Claude)

This double setup used to work wonders, but I want to gain back access to Qwen code and ideally on my machine

The issue is that I don't understand what 40t/s means... I want to ask people who actually code review with local LLMS, would my setup work ? Or will it be annoying and slow ?

I’ve been building a contract tracking SaaS over the past few weeks — something to stay on top of renewals, payments, obligations, all the stuff that usually slips through.

What I didn’t expect is how I ended up using it.

I almost never open the dashboard.

I just ask things like “anything renewing soon?” or “what payments are coming up?” and get what I need back. That’s basically the product now.

The UI is still there, but more as a fallback when I want to double check something or dig deeper.

It made me realize the interface is shifting. Not in a hype “agents replace everything” way, but in practice — if I can just ask and get an answer, I won’t go click around a dashboard.

The part that still feels unsolved is how these agents actually operate across systems. Everything today relies on API keys or OAuth, which basically means whoever has the token can act. That gets weird fast when you have agents acting on behalf of users across multiple services.

Feels like we’re missing a proper trust layer for agent-to-agent interactions.

Curious if others here are building in this direction or thinking about this differently.

I currently run a Nvidia RTX 5090(32VRAM) with 32GB DDR4 RAM and a AMD 5950x , I want to run local models , but not sure what to go with , mostly for coding. I am currenlty runnig Claude Pro but hitting the limits too quickly. What are yall running and what are yall using it for ?

The code I was working on hit a brick wall and I was stuck. Claude fixed the problem and got me back on track in 15 minutes. The algorithm was able to perform without breaking. So it now optimizes the tone and put the contours where they are needed in a more efficient way.