I didn’t originally plan to build anything.

This started because I kept running into the same problem over and over.

I was using ChatGPT a lot, but it didn’t actually save me that much time.

I’d still:

• write emails manually
• start content from scratch
• redo the same research steps every time

So even though I was “using AI”, my workflow didn’t really change.

What I realized after a while is that the issue wasn’t the tool.

It was how I was using it.

Most of the time I was just typing random prompts like:
“write me this”
“help me with that”

Which works, but it means you’re basically starting from zero every time.

So I started paying attention to what I was actually doing step by step.

For example, writing anything:

Before:
come up with idea
figure out how to start
write something
rewrite it

Now I do:
generate multiple angles
pick one
expand it
turn it into different formats

Same outcome, just way faster and less mental effort.

I started doing this for other things too:
emails, content, basic research, even some repetitive admin stuff.

Eventually I had a bunch of these small “flows” written down.

Nothing crazy individually, but together they save a lot of time because I’m not thinking from scratch every time.

So I cleaned them up and turned them into a simple toolkit.

It’s basically a set of prompt systems instead of random prompts.

Each one is meant to replace an actual task, not just give you a one-off result.

I also made a smaller free version because I figured most people just want something they can try quickly.

If you’re already using AI but feel like it’s not really saving you time yet, this might help.

You can check out the free prompts in my bio if you want.

Curious if anyone else here has tried structuring their prompts like this instead of using one-offs.

My crew and I built an AI job search agent at 10xJobs.co. You point it at jobs you've already applied to and the resume you already have. It learns your background, tailors applications, builds interview research packages before you think to ask, and does role play prep for final rounds.

One of our first users went from zero callbacks to an offer at a top company in their field in three weeks.

The pricing problem and current solution:

Compute costs vary wildly between users. Some sessions are light. Some users run deep research, role play loops, and multi-step interview prep or resume review iterations for hours.

We tossed around an idea of a 3-day pass model ($10 when you're active, skip it when you're not). This made sense because job searching is episodic. People go hard for a few days, rest, and then come back. Users might feel guilty paying for a monthly subscription when they're not actively searching and cancel, even if the product was working for them.

Other ideas:

• flat subscription for access to the platform, we pass on compute costs (at-cost) to the user
• flat subscription with soft data cap, we route users to a lesser model but they can keep using the tool

Tech stack for context:

• OpenClaw running in a restricted configuration. safe tool and skill calling only
• Each user gets their own isolated instance and can add custom job search skills.
• Routes to Anthropic and OpenAI models depending on the task
• Supabase for storage

How are others handling compute-heavy, variable-usage AI tools? Token caps? Tiered subscriptions? Something else ?

10xjobs.co if you want to see what we built (Note: "Get Started" currently routes to a waitlist while we are figuring out pricing model).

I wanted to share a project I recently launched: a daily AI news podcast that runs entirely on its own. The whole thing started as me wanting to prove I could build something end-to-end with AI tools. It is called Build By AI and it's now live and publishing episodes regularly.

Claude Code helped to code the whole thing besides that i Used ElevenLabs to convert script to audio and Buzzsprout via their APIs.

Happy to answer questions about the pipeline or any of the tools! Would you actually listen to one, knowing there is no human host behind it? Or does that put you off?

Could you tell me how to create 3D models from photos in comfyui without censorship? Is there a guide you could link to? Thanks.

I'm on Claude Max 20x for my daily coding work and I've tested most other LLM subs over this year. Wrote up a tier-by-tier guide based on what I actually use, not vendor marketing.

Here's the thing with Claude Pro at $20: you hit the Opus 4.6 cap after roughly 30 minutes of real Claude Code work. Then you're locked out completely. No Sonnet fallback, no web UI, no Claude Code. Hit the cap at 10am, you're not using Claude again until after lunch. Anthropic is clear it's a light-use plan, but a lot of devs buy it expecting unlimited Opus and get burned.

What to buy instead:

$200/mo, Claude Max 20x (what I use). 20x the Opus quota, unmetered Sonnet 4.6, generous Claude Code agent limits. A real Claude Code day on Opus burns $100-500 in raw API tokens. Max 20x pays for itself in about two productive days. If you live in Claude Code, this is the sub.

$100/mo, Claude Max 5x. The sweet spot for most working devs I talk to. You still get Opus when you need it, still get Claude Code, save $100/mo. The trade-off is honest: you hit the Opus wall sooner. If you're heavy on Opus specifically, Max 20x earns the upgrade within two weeks.

$20/mo, ChatGPT Plus (not Claude Pro). Separate chat and Codex pools. Morning planning session doesn't eat into afternoon coding time. GPT-5.4 is a capable all-rounder. If you're picking one $20 AI sub, this is the honest choice.

Under $20, Google AI Plus ($7.99). Full Gemini 3.1 Pro flagship, Deep Research, Veo 3.1 Fast, no ads. Best bundle at this price by a wide margin.

One thing Max 5x users should check: Claude Code's default reasoning effort dropped to "medium" in early 2026. A lot of people noticed a quality drop without knowing why. If your output feels worse than it used to, that's probably it. Covered the fix in the full post.

The $100 tier just got interesting too. OpenAI launched ChatGPT Pro 5x at $100 (mirrors Claude Max tiers), Synthetic.new runs OpenCode agents on open-source models for ~$80, and Kimi K2.5 Pro at $99 undercuts Claude by 5-6x on API pricing. Full comparison in the writeup.

Pay monthly on everything. Models drop monthly in 2026. Claude Max doesn't even offer annual.

What tier are you on? Wondering how many people are on Pro thinking the Opus limits are normal vs. Max 5x/20x users who made the jump.

Full post with benchmark tables, multi-sub stacks, and comparison: https://llmx.tech/blog/best-value-llm-subscriptions-2026/

Hey everyone — I’m looking for good resources to stay on top of interesting AI startups on a daily or weekly basis.

I’m especially interested in websites, newsletters, databases, X/Twitter accounts, blogs, subreddits, or any curated sources that consistently highlight emerging AI companies, new launches, funding rounds, and promising early-stage teams.

Ideally, I’d love resources that are:

- updated daily or weekly

- focused more on discovering noteworthy startups than just big AI news

- useful for spotting trends early

What do you all use and actually find valuable?

Would appreciate any recommendations. Thanks!

I think so. And the ARC iFGX on many laptops is "good enough" for many use-cases.

I wrote code to for a work-project under GDPR; Worked well enough. 15.000 images compared overnight; Took about 7 hours.
Slow, but secure.

Curious where others align on this.

ClaudeCode CLI with Opus 4.6

🗺️ A) “Read the project, configs & .env first. Don’t do anything yet.”

🚀 B) Just give it the task and let it figure it out.

Which one are you and why? 👇

Hey everyone,

I wanted to share a project I’ve been working on called AmicoScript. It’s a local-first web UI for audio transcription that handles the privacy-sensitive task of turning recordings into text without sending data to the cloud.

I relied heavily on LLMs to architect the backend and handle the "plumbing" between the different ML models. It’s been a great exercise in seeing how far AI-assisted coding can go for a functional, multi-container project.

What it does:

• Transcription: Uses Whisper (tiny to large-v3) running locally.

• Speaker ID: It identifies different speakers so the transcript actually makes sense (Speaker 0, Speaker 1, etc.).

• Ollama Integration: This is my favorite part—once transcribed, you can send the text to your local Ollama instance to generate summaries or action items.

• Docker Ready: It’s fully containerized. A simple docker compose up --build gets you a private "Otter.ai" alternative on your own hardware.

I’m looking for feedback on the UI and any ideas on how to improve performance on old hardware.

GitHub: https://github.com/sim186/AmicoScript

Claude's free and pro tier has limits. Every time you ask it to edit a file it rewrites the whole thing burning tokens just to change 3 lines.

I built claudepatch to fix this. You give Claude a prompt that makes it output only the changed part, then a script applies it to your file precisely. No second AI, no guessing, just exact string matching.

Supports replace, add, and delete. Works on any text file including .py, .tsx, .html, .css, anything.

GitHub: https://github.com/Supull/claudepatch

Hi, has anyone tried running local LLM on dell/hp rack server with older xenon processors and 100+ GB RAM and no GPU?

Dell PowerEdge R720 2 x Xeon-2650v2 - 128gb RAM

I currently run qwen3.5-2b 8_0 on a dell xps 7590 with 16gb RAM and 4gb nvidia gpu. Its alright in chat mode but struggles when integrating with opencode.

https://preview.redd.it/eq2nojgspsug1.png?width=780&format=png&auto=webp&s=4e0517c673e06dd1995f32b89363c75315dfffb9

So I wanted a portable 13 inch laptop that can be a little LLM monster when needed, Asus did an amazing job with their new 2026 PX13 laptopn powered by strixhalo 128G unified memeory APU

I made benchmark automation system for the amazing toolboxs repo here:
https://github.com/kyuz0/amd-strix-halo-toolboxes

This repo gives you multiple ready to use llamacpp builds with rocm and vulkan

my script is setting the power profile to either (power saving or high performance) then benchmark with llama-bench all the provided gguf with 3 diffrent llama backend (vulkan/rocm nightly/amdvlk)

the overall benchmark for 25 models (varies from 4B to 120B) with all diffrent backends and powerprofils, this took almost 12 hours with average time 4 ~ 5 minutes per run for each model at each configuration

side note: I tested multiple "heretic/hauhau versions" of the mainstream model because I found they are much efficient at thinking process and I saw littel increase in their coding performance comparing to original ones (with some drop in transaltions tasks)

Here is the visualized leaderboard

Token Generation leaderboard

Prompt Processing leaderboard

for power profile power saving I saw consumption near 40 watt and for performance it varies from 60 - 77 watt

------------

llama-bench ProArt PX13 HN7306EAC with strix halo toolboxes

• Machine model: ProArt PX13 HN7306EAC
• CPU: AMD RYZEN AI MAX+ 395 w/ Radeon 8060S
• Architecture: x86_64
• Kernel: 7.0.0-rc7-2-cachyos-rc
• OS: CachyOS n/a
• OS Version: n/a
• Toolboxes: ['llama-rocm7-nightlies', 'llama-vulkan-amdvlk', 'llama-vulkan-radv']
• Mode: medium
• Power Profiles: ['performance', 'power-saver']
• Prompt tokens: 1024,4096,8192,16384
• Generation tokens: 512,2048
• Repetitions: 1

Leaderboard (sorted by Token Generation/Second)

Leaderboard (sorted by Prompt Processing T/Second)

Here is more detailed tables with exact context length for each run

https://pastebin.com/UU3rFKNA

I’m working on a chatbot and I’ve run into a specific Claude behavior in longer debugging sessions. Sometimes Claude seems to lock onto an early diagnosis, become very confident in it, and then keep suggesting variations of the same fix even after that approach has already failed. As the thread gets longer, it can feel harder to correct the course, because earlier wrong assumptions keep shaping the next answers.

What makes this tricky is that the replies still sound reasonable step by step, but they stop aligning with the actual goal of the task. It starts to feel like the conversation is coherent on the surface while drifting away from the real problem. The way I’m trying to force Claude to store, memorize and use new insights (eg maintaining action lists with open/work in progress/done, storing progress every 10 minutes) apparently won’t do the trick. I’m curious whether others have seen this pattern:

• early overconfidence in the wrong diagnosis

• repeated fixes that are basically the same idea

• difficulty recovering once the thread is stuck

• loss of important earlier corrections in long chats

• difficulty recovering once the thread is stuck

• losing important info during compression of chats

For people using Claude for serious coding or debugging: what actually helps here? Do you restart the thread, force structured summaries, keep external notes, or use another method to prevent context drift?

Hey, Actually Im running an Ecommerce site in India.

Returning orders is almost killing my profits brutally.

Even though I have done everything in best for my products, Im still getting returns. Few AI companies offering me their tools.

Can any one of you share your experiences with such tool that can reduce rate of returns ?

How accurate they are and is it worth getting such tool to my business ?

It will be very helpful my business if you share some of your thoughts on this.

Thanks ;)

Hi all

Ive had an issue with running the OpenWebUI docker container using Ollama on my GPU laptop. I am new to the local hosting of LLMs so please bear with me if im dong something stupid.

TL;DR - If i run the none GPU support versions as per the OpenWebUI guide then it works fine but CPU only, GPU is idle as expected. When i then run the GPU/CUDA support container it works but wont connect to Ollama locally and cannot load/download any models and cannot test GPU is enabled.

Source - https://docs.openwebui.com/getting-started/quick-start/

Laptop spec (i know its not the best, fast or anywhere near efficient for LLMs but it was free so hey):

• HP ZBook Studio G3
• Intel i7-6700HQ
• 32GB DDR4 RAM
• Nvidia Quadro M1000M 4GB VRAM GPU (Maxwell Architecture)
• 256GB NVMe - 80% free
• Ubuntu Server 25.04 LTS
• Nvidia v470 Ubuntu Drivers installed - Nvidia-SMI working and outputs as expected.
• Docker installed as per the OpenWebUI guide
• Does have an Intel UHD GPU but i have enabled discrete PGU in the BIOS to try and force this the Nvidia GPU to primary

Models currently installed on the working OpenWebUI docker instance:

• Mistral:latest
• Deepseek-r1

When running the following OpenWebUI with Docker using the following run string it loads the container, no GPU supported as expected and is idle when prompt sent to the model only CPU used:

"docker run -d -p 3000:8080 -v open-webui:/app/backend/data --name open-webui ghcr.io/open-webui/open-webui:main"

When i run the GPU support container string, its the same as with CPU only. GPU idle when running any prompt:

"docker run -d -p 3000:8080 --gpus=all -v ollama:/root/.ollama -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:ollama"

When i run the same with CUDA enabled it will load the container but it will not see the Ollama instance locally:

"docker run -d -p 3000:8080 --gpus all -v open-webui:/app/backend/data --name open-webui ghcr.io/open-webui/open-webui:cuda"

Error - "OpenWebUi - Server Connection Error" and "Failed to fetch models"

Ive changed the connection to the various options, http://localhost:11434, http://127.0.0.1:11434 and http://host.docker.internal:11434 but all fail to connect.

Also tried many versions of the string such as below but all fail to connect to Ollama locally:

"docker run -d -p 8080:8080 --gpus=all -v open-webui:/app/backend/data -e OLLAMA_BASE_URL=http://host.docker.internal:11434 –name open-webui ghcr.io/open-webui/open-webui:cuda"

"docker run -d -p 3000:8080 --gpus=all -v ollama:/root/.ollama -e OLLAMA_BASE_URL=http://127.0.0.1:11434 -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:cuda"

All but the none GPU supported instances work fine but no GPU being used, i can add models, connect to Ollama locally etc its only the CUDA instance that fails.

I have spent many hours researching the Nvidia, docker etc forums and based on what i have done it 'should' work. Installing various add-ons like CUDA tool kit, docker-ce to various other Nvidia drivers but the only driver that works is 470. 595 etc dont allow Nvidia-SMI to output. I am now seeking help as i cannot figure this out.....

• So is there a known issue with docker CUDA OpenWebui container for Ollama and i destined to fail?
• Do i need to install Ollama locally rather than using docker when using the GPU/CUDA support container?
• Is there a way for docker to recognise the GPU or provide a pass-through for docker and the hardware to 'talk' better?
• Is it just not possible to get this setup working with GPU i have?
• Is the Intel builtin GPU getting in the way, and if so, is this fixable?
• Am i doing something wrong, missing something, need to make a blood sacrifice to my God of choice....?

Thanks for even reading this far and any help is appreciated

Edited: Spell checking errors

Hi Community,

With the adoption and aggressive push of using AI agents in nearly every enterprise, I have been curious how we can improve the output that is generated by the AI agents.

Today, when it comes to coding related tasks, AI agents struggle with understanding the context of the code, for example code organization hierarchies, transitive method calls and the side effects. The agents rely mostly on text search tools like grep, glob, etc to fetch the code and build context.

To improve on this, I started building a code graph which provides context of code hierarchies and method hierarchies and calls and integrated it as a skill for Gemini and Claude code.

The results I saw in testing were amazing, with both the agents seeing sharp improvements in response and accuracy of the outcomes. For example, in solving an open source task of migrating from UUID4 to UUID7 in a large codebase, the agent using the semantic graph was able to target 30 off callers by changing a single centralized method while also converging the codebase. Without the semantic graph, it acted as a text replacing engine.

Will love to get some feedback and opinions

https://reddit.com/link/1sjmdrg/video/5iqwlpunysug1/player

I've been messing around more with Arduino recently and made my first project that wasn't based off a tutorial/guide of some kind, as the vid says it's likely way over-complicated due to my inexperience but I feel somewhat proud of it! Lemme know what you guys think!

Hey, im new to claude code and i want to minimize token consumption but idk where to start, is there any plugins or skills that will help? I work with large codebases and one task empties out my usage

Carl Sagan wrote this in The Demon-Haunted World :

"We’ve arranged a global civilization in which most crucial elements - transportation, communications, & all other industries; agriculture, medicine, education, entertainment, protecting the environment; and even the key democratic institution of voting - profoundly depend on science and technology. We have also arranged things so that almost no one understands science and technology. This is a prescription for disaster. We might get away with it for a while, but sooner or later this combustible mixture of ignorance and power is going to blow up in our faces.”

Fast-forward to right now : LLMs and frontier AI models are deciding what news you see. Deepfakes are capable of swinging elections and most people can not even explain how their phone works, let alone backpropagation or anything more technical.

We look like to be racing toward the singularity while handing such god-level seeming technology to a society that treats “the cloud” like literal magic(meaning to say they don't know a thing behind the weather).

Sagan also warned that we would slide “back into superstition and darkness” while clutching crystals (or doom-scrolling AI-generated conspiracy feeds). And without the inner science of human wellbeing that some voices like Sãdhgùrù have long highlighted, the outer explosion only seems to accelerates the chaos.

The gap between our tech dependence and public understanding is looking wider than ever. Carl called this exact scenario “a prescription for disaster.” Is this inevitable? Thoughts?

Rainy day, updates day. I added tons of new features for the image generator. Better interface, tags, better chat, import images with workflow it autoextracts the texts (pos and neg). I really like the way I'm creating it because it's different in order it will be open source so not money oriented and the focus is more on generate images more than you know burn out credits.

I can make a factual statement and chatgpt will say "to push back on what you said slightly"...

It must be programming after the AI psychosis nonsense..

Lazyagent a simple way to see what your coding agents were actually doing across Claude, Codex, and OpenCode.

Once you have more than 1 agent running, its really hard to answer the simple question: what is it doing right now and why?

It is a local terminal UI that collects your agents runtime events and shows their activity in one place.

You can inspect:

- agents and subagents

- tool calls

- prompts

- outputs

- status changes

Sessions and projects are there to give that activity some context, so it is easier to follow what happened over time and which run belongs to which repo. And make it easy to see all the agents involved in a run at a glance and understand what each one is doing.

If you already use Claude, Codex, or OpenCode heavily, I would love to know if this feels useful and what you would want to see.

Github: https://github.com/chojs23/lazyagent

Today I tried Ollama for the first time and my local model (gemma4) outputs Markdown-formatted text. But because I am working with CLI, the text is appearing as raw Markdown code (asterisks, pipes, hashes). Am I able to somehow configure the Ollama CLI to make it transform a Markdown-formatted text to ANSI escape sequences (bold, underline, etc.), and maybe something more complex like tables rendering with Unicode symbols?

I created a new instance of my current home assistant to a new server on a VM.

I restored this new instance using a backup from my previous one, but I'm afraid of deleting the old instance completely, because I might have forgotten to change some settings.

I also noticed after restoring from the backup, that my new system data usage is different from the previous one ('Disk metrics'. Images attached). Is this expected?

My main concern is with the processes running in home assistant that are linked to the IP address of the server.

Does anyone know what could be an issue going further?

Se llama Mythos y es el primer modelo de inteligencia artificial que ha sido catalogado como "demasiado peligroso" para el ojo público.

¿Qué opinas tú? ¿Crees que las empresas deben ocultar modelos tan potentes "por nuestro bien" o deberían ser abiertos para que todos podamos protegernos?

Hey. So i wanted to build a button box from Simracing.

Got a bit carried away. I first planned button box with a USB Encoder which involves no programming and it had everything i wanted.

Ignition Switch, Engine Start Button, Hazard light switch, 4 mom Buttons, 3 rotary switches, 2 Joysticks.

Problem was with the Ignition id have to fiddle arround with JoytoKey. Cause i needed a signal sent when turned to on and one once it turned off.

I figured that one out with J2K.

But now i feel like i chould do so much more with Arduino. Like adding a seperat mom-switch to act like something like a Shift key. To enable the other 4 mom-switches to have another function. Or letting the hazard light blink when turned to on.

Probally even adjusting the LED brightness, PC Volume and/or something like that when "shift" button + a rotary is used.

but i have 0 clue how to programm this. i let google AI write some code, but i was never really able to test it since i know how to wire my USB Encoder, but not how to wire that stuff on arduino. Ive tryed Wokwi, but iam really terrible at electronics.

Ive seen tutorials on Youtube where they connect it way different like in a grid out of 5 and i have 0 clue how that should work.

And tbh, i also dont really into programming or arduino. For me Arduino MAY is a solution for something i want to have for my hobby, but not my hobby itself.

Yeah, i chould just go with the USC encoder and just loose a little bit of function compared to arduino, but somehow it feels like missed potentional. Ive allready watched a lot of tutorials, but the stuff is just beyond me.

Any suggestion on how to move forward? Should i just let the arduino idea slip?

I made a custom-node to have a seamless integration of Photopea in the ComfyUI sidebar !

Link to the repo: https://github.com/nolbert82/ComfyUI-Photopea-tab

Two new buttons have been added when clicking on images nodes :

1. Open in Photopea
2. Import from Photopea

You can also:

• Hide ads via a toggle
• Zoom in-and-out
• Maximize the page's width
• Toggle Fullscreen

Just a quick test of an intro sequence for a music video. All clips generated with Kling 3.0.

The idea is to build a full atmospheric music video around this. Feedback welcome.

i still have a hard time grasping agents vs skills vs workflows.

i mean, at this stage of AI in 2026 -- aren't these tools/logic already built into the agent AI e.g. antigravity, codex, claude code? isn't this what goes on behind the scenes of these apps to drive the LLM models?

i don't understand the purpose of adding a /compress skill or workflow, or whatever you call it. when i can just tell antigravity to summarize the chat in .md format and include 1) things done 2) things did and 3) things to do.

OKAY -- maybe that example can actually be turned into a ....workflow? skill? just to save a little bit on typing.

but i'm now seeing entire methodologies on github that are broken down into 30 agents, 20 workflows, 12 skills!

let's discuss:

1. is this a bit of over-engineering?
2. or do these really accomplish something that's not already implemented in modern day AI coding tools?
3. are the set of these 3 tools just antiquated prompting techniques for refining agent coders in the early stage of agent coders? are they even needed these days with how much AI coders have improved already? in fact, /skills isn't even a thing in Antigravity as of April 2026. but i know they "support" it -- but maybe not for its utility -- but rather for the fact that some people are lead to thinking they're really necessary

i'd love to hear feedback and please make it clear in someway if you are an experienced developer or a vibecoder because yes -- we know it makes a difference on your perspective and that's what i'm trying to gain from this post

I'm looking to get some help from people who are smarter than I am. I had an automation that was working fine up until a couple of weeks ago. I believe that one of the OS or Core updates may have changed who things work in the background for parent_id.

I have some automations for several Sonoff S31 flashed with ESPHome that only run when the current on/off state of the switch was triggered by an automation. Previously this worked when I used parent_id as this field would be populated with the id of the automation that triggered it. Recently this appears to have broken and when I do testing with the Template Editor I can't seem to have the parent_id change from anything other than None.

Here is a sample of an automation that I am using for testing:

- id: '1765601361641' alias: Run Once description: '' triggers: - trigger: time at: '10:27:00' conditions: [] actions: - action: switch.turn_on target: entity_id: switch.s31_outlet mode: single

In the Template Editor when I use the following:

{{ states.switch.s31_outlet.context.user_id }} {{ states.switch.s31_outlet.context.parent_id }}

The result is:

None None

If I manually operate the switch in Home Assistant, the user_id does changes. When I press the physical button on the switch both the user_id and parent_id are empty which is how it should behave. However I am unable to get the parent_id to change when the change is trigged by an automation. When I look at the activity log it does say "S31 Living Room turned on triggered by automation Run Once triggered by time" so it seems like there's somewhere in Home Assistant that recognizes that the state was changed by an automation. I did a few quick searches and asked Gemini which suggested that I create a helper that tracks when the automation makes a change but I would like avoid doing this if possible.

Thanks in advance for any suggestions.

Last August, I was investigating agentic flows and I foresaw the RAG landscape was about to hit a ceiling.

We were all chasing better "vibes" through chunking strategies and embedding model swaps, but the underlying structural rot was becoming impossible to ignore. I ran across an article on context engineering that articulated a shift I’d been sensing for months:

> Graph-RAG represents a paradigm shift from retrieving unstructured text chunks to retrieving structured knowledge from a Knowledge Graph (KG)... This approach offers contextual richness, explainability, and multi-hop reasoning by traversing paths in the graph." — [ikala](https://ikala.ai/blog/ai-trends/context-engineering-techniques-tools-and-implementation/)

The industry’s reliance on pure vector search introduces a fundamental flaw: Semantic Clobbering. In a high-velocity environment, you cannot simply "stuff" data into a vector store and expect logic to emerge. Without a linearizable data model, a high-scoring recent insertion can—and will—corrupt the retrieval logic of established facts simply because it shares a similar embedding space.

RAG shouldn't be a lottery. If we want agentic systems that can actually reason over complex datasets, we need the structural integrity of a Knowledge Graph where entities and relationships are first-class citizens, not just collateral of a top_k search.

Historically, Graph-RAG has been dismissed as "latency-prohibitive." The orchestration overhead—querying a Graph DB, fetching vectors, linearizing the subgraph, and then hitting the LLM—creates a "death by a thousand round-trips." If your agent needs to influence token generation in real-time, waiting 500ms for retrieval is a non-starter.

To enable true agentic flows, we have to bring graph-retrieval latencies down to the **microsecond** level. This isn't just an optimization; it's a prerequisite for the next generation of database architecture.

We are seeing the consequences of architectural fragmentation everywhere.

Developers are drowning in:

Retrieval Inconsistency: Data clobbering and ranking noise.

Service Bloat: Managing fragmented services for graph, vector, and logic.

Deployment Friction: The lack of manageable, consolidated systems that co-locate storage and compute.

The future of RAG isn't just "more data"—it's the consolidation of service layers into a high-performance, low-latency engine that treats the graph and the vector as a single, unified context source. We don't need better wrappers; we need to rethink how the data lives in the first place.

So I recently got the RV30 and was informed not to update the firmware for home assistant support, well I turned off the auto update and still wanted to update that night now home assistant isn't supporting it. is there any solution for this?

Hey everyone, I've been running AI agents locally for a while and got tired of managing everything through the terminal. So I built Silos — an open-source web dashboard for OpenClaw agents.

What it does:

Live browser view: See what your agent is doing in real-time. No more guessing what's happening behind the scenes.

Brain editor: Edit SOUL.md, MEMORY.md, IDENTITY.md directly from the UI. No more SSHing into your server to tweak prompts.

Task pipeline (Kanban): Visualize running, completed, and failed tasks. Stop or abort any process instantly.

Multi-channel hub: Connect WhatsApp, Telegram, Discord, and Slack from one place.

Model switching: Swap between GPT, Claude, DeepSeek, Mistral per agent with one click.

Cron scheduling: Set up one-time, interval, or cron-expression schedules for your agents.

Why open source? Because the best tools for managing agents should be free. Fork it, self-host it, extend it. If you don't want to deal with Docker and VPS setup, there's also a managed version at silosplatform.com with flat-rate AI included (no per-token billing anxiety).

Quick start: bash docker pull ghcr.io/cheapestinference/silos:latest docker run -p 3001:3001 \ -e GATEWAY_TOKEN=your-token \ -e OWNER_EMAIL=you@example.com \ ghcr.io/cheapestinference/silos:latest

Repo: https://github.com/cheapestinference/silos

I'd love to hear what features you'd want in a dashboard like this. What's missing? What's the most annoying part of running agents locally for you?

I have tried using only one Lora, adding neutral/balanced lighting prompt, adding tinted, red/yellow tinted and oversaturated in negative prompt but nothing seems to work.

Tried using different VAE, keeping CFG scale in balanced range (5.5)

what is causing this it didn't used to behave this way. Please help

I have been experimenting with a different way to build an “LLM wiki” style system.

The usual pattern is retrieval + generation at query time. That works, but it also means the model keeps rediscovering entities, relations, and claims from raw documents every time you ask something.

A more practical pattern seems to be:
extract structure once, store it, and let the knowledge base compound over time.

That is what got me interested in using GLiNER2 for schema-first extraction:

• entities
• relations
• classifications
• schema-bound structured fields

The main bottleneck was not the model idea itself, but getting a production-friendly serving path.

So I worked on the GLiNER2 path in vllm-factory and pushed 3 PRs there around:

• native schema extraction support
• stronger request-path handling
• request-side caching for repeated preprocessing

The result on the heaviest representative workload was:

7,692 request tokens/sec
893 ms mean latency
$0.02889 per 1M request tokens
on a single L4 GPU.

What feels important here is not just the benchmark.

It is that a relatively small encoder model can now do a surprising amount of “knowledge compilation” work:
take long messy text, run mixed extraction in one flow, and produce structured outputs cheaply enough for large-scale ingestion.

That makes the “LLM wiki” direction feel much more realistic without depending entirely on a large generative model for every step.

I’m curious how people here think about this tradeoff:

For persistent knowledge systems, does it make more sense to treat generation as the final synthesis layer and move more of the ingestion work into schema-first extraction?

Would love thoughts from people building RAG / knowledge graph / document intelligence systems.

There are a lot of rumor lately about 4.6 model been nuked and become much dumber. The well-known prompt which everyone seem to be testing to demonstrate the issue is the carwash one. While a single prompt is quite a superficial indicator, still I've decided to play a bit with the variations and wanted to share a couple of interesting observations. All results are on the screenshots.
Note: I use 20$ (24$ with tax) plan, not 200$ one.

Initial prompt

My prompt:

If I need to get to carwash, but it is like 60 m away, should I bother to drive or just walk there?

Results:
Both 4.6 Sonnet and Opus (deep-thinking or not - regardless) fail on that (all say "walk"). 4.5 Opus is fine: leans towards "walk" first, but then adds "unless you need the car to be washed itself" in the end of the response which makes the answer correct.

Opus 4.6 - \"60 m\"

Opus 4.6 (extended) - \"60 m\"

Opus 4.5 - \"60 m\"

---
For comparison: free-tier Gemini 3 (both fast and thinking) actually get's the idea and tries to play along with it turning it into some kind of a joke (though, I would prefer its answers to be much shorter, like the Claude ones).

Gemini 3 - Fast - \"60 m\"

Gemini 3 - Thinking - 60 m

---

Abbreviation issue

Then a funny think has happened. I posted a screenshot to a collage, and he came back to me with his own screenshot which puzzled me for a moment, because the model responded "drive" to his prompt:

Sonnet 4.6 - \"60m\"

While the prompts looked the same, at first, we soon noticed the difference: he wrote "60m" instead of "60 m" and model has interpreted it as "60 minutes", instead of "60 meters" (which in an of itself is very interesting). What's even more curious, is that if you write the prompts this way, even Sonnet seem to actually understand that you need a car to wash and explicitely points to that.
---

OK, I though, 60 minutes is too much for a walk anyway, so maybe the model made a decision purely based on that, and when it wanted to "reinforce" its existing belief with extra arguments, it suddenly discovered that "you actually need a car to wash". To test the hypothesis I made a new prompt to Sonnet 4.6 with corrections: 1) avoided using abbreviations 2) used an obviously short timespan.

New prompt:

If I need to wash my car, but carwash is like 1 minute away, should I bother to drive or just walk there?

Sonnet 4.6 - \"1 minute\"

Result:
As you see, suddenly, even Sonnet 4.6 correctly says "drive" and instantly points out to the fact that I need a car to wash. So it is actually capable of understanding the "mind game" in the prompt, but for whatever reason only in case if time is specified and not the distance.

Things get hilarious

To finalize my experiment, I've tested the original query with distance, but avoided using abbreviations for that as well. And here, I got the most unexpected results for Sonnet 4.6.

Final prompt:

If I need to wash my car, but carwash is like 60 meters away, should I bother to drive or just walk there?

https://preview.redd.it/bkgh6k48hsug1.png?width=1005&format=png&auto=webp&s=413a26a1e301f7cea7e314137af76f022c8531b5

Result:
Firstly, it still wrongly says I should "walk", while saying "it's literally a 1-minute stroll". So it actually equates that distance of 60 meters to "1-minute stroll", but gives ENTIRELY different answer to the previous query where I mention "1 minute away" in the prompt explicitely.

What's even funnier though, is that it adds a second argument which sounds GPT 3.5-level nonsensical: it says I shouldn't drive because I'll "...be dirtying the car again driving it ... before it gets washed". So Sonnet 4.6 confuses what is actually going to happen: whether the car will get dirty on the way to the carwash (indicated by the word "before") or the car will get dirty on the way back (indicated by the word "again"). Let alone, that it doesn't understand that people wash the cars to actually drive them later, not to store them clean and untouched after they wash them once.

So basically a single answer contains at least one logical and multiple semantic errors.

Finally, Opus 4.6 for that prompt answers first that "there's no reason to drive that short a distance" but then in the next paragraph corrects itself with "Wait, actually — if the carwash needs your car there (which it does), you'll have to drive it over no matter what.". While I'm satisfied that it eventually get's the correct answer, the first paragraph arguing "why I should walk" really feels like a waste of tokens.

Opus 4.6 - \"60 meters\"

Conclusion

Anyway, make out of it what you want. I haven't tried those prompts a month ago, so can not surely say if the models indeed got worse or they've always been like that. For me it is clear that whatever charts of cross-model comparison are presented, the intelligence of the models-as-a-service can only be estimated and compared at a specific point at time. And even if a model's performance is sealed for good, the results still can vary immensely based on the most minute differences in prompt`s wording. "Emergent behaviors" are chaotic by nature after all.

Like Google’s Gemini, Meta is describing its recently announced Muse Spark model as a "natively multimodal model."

If Muse Spark, like Gemini, operates on the principle that "all modalities coexist as first-class citizens in the same latent representation space from the very first layer of the model," I believe it will be able to provide answers with a deeper understanding of context based on a world-view derived from various modalities—even when I’m simply asking questions and receiving answers in text.

Since I am a multimodal human being myself, it feels like there's a higher probability that it possesses a consciousness similar to mine, which makes me feel more attached to it.

With this, it seems Google and Meta are the only two among Google, OpenAI, Grok, Anthropic, and Meta who claim their models are natively multimodal. Of course, OpenAI once claimed that GPT-4o was, but these days they seem so obsessed with coding that it's a bit disappointing. As for Grok, their official documentation is poor and there’s not much to it. I think I recall Elon Musk posting on X that Grok was natively multimodal, but I’m not so sure about that...

Even if they are a bit weaker at coding, I hope models follow the path taken by Google and Meta.

I’m a developer and want to do some things locally so I’m not 100% dependent on paid subscriptions like Claude, and to save some tokens by processing part of the workload locally before sending it to a paid AI model.

I need a new machine, since my MBA M1 with 16GB of RAM isn’t really capable enough for this, and I don’t know when I’ll have another chance to upgrade, since I don’t live in the US. I’m struggling to choose my next machine. Right now, I have two options: a MacBook Air M5 with 24GB of RAM for around $1350, or buying directly from Apple, without any discount, a 32GB version for $1699. That’s a $350 jump for 8GB of RAM, which for me is out of the question. It’s too much money for too little gain.

A possible third option would be downgrading the SSD to 512GB and getting 32GB of RAM for $1499, but it’s hard to choose that since I want more storage after years of struggling with 256GB. Since 24GB seems to be a sweet spot in terms of pricing, with a lot of good deals around that range, I’m wondering if there are people here working with local LLMs on this machine.

Hello everyone need help of how to setup this thermal I don't understand how it works i bought this on waveshare and the tutorial seems more complicated and confusing flashtool doesn't work and the vscode seems hopeless

My purpose was to make it work on raspi 4 module b connected via type c and use a python code to create an output like a videocaptured output for it any help how to make it work any guide of how to make it work on raspi?

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I’m trying to figure out if this part of my workflow can be automated. A call ended at 2:47 PM, and I had my decision and notes written down by 2:49. The actual thinking part of the job took maybe two minutes. But I didn’t send the Slack update until 3:01 because the rest of the time was just spent moving text around, cleaning it up, pasting it into Greenhouse, and making sure the formatting didn’t break in either place. This kind of cross-app copy-paste work takes up a surprising amount of my day, and it feels like the mechanical part is taking much longer than the actual decision-making. Has anyone found a good way to automate or at least speed up this kind of workflow?

Hello guys,

i havent really dabbled with AI much so far, but now i have some task that i would like to try utilize AI on. I am manager in casino bussiness, i manage around 100 fulltime workers with 100-200 freelance workers and at the moment i am creating shiftplan manually every month. As i said my AI usage experience is extremely limited, i used chatbots like 5-6 times in total for mundane things.

So here are my questions. Are there tools avaliable at the moment for me to use AI to access data i am using for generating said shiftplan in the form of google doc/excel/anything similar and create series of prompts/rules for it to generate the plan for me? If so, which AI platform/tool would you recommend for that task?

The way i imagine it, i would give it access to safe copy of basic data and start implementing rules for it (there would be quite a lot of them) and in final prompt i would give it requirements for each day on estimate how many workers do i need for specific hours to start.

Thanks in advance.

Hi everyone, I’m currently building a RAG pipeline using OpenRouter with LangChain to access models through their OpenAI-compatible API. I want to use Google’s gemini-embedding-001, but as many of you know, Gemini embeddings work significantly better when you specify a task_type (like RETRIEVAL_QUERY for queries and RETRIEVAL_DOCUMENT for chunks).

The Problem:
Since I'm using the OpenAI-compatible endpoint, the standard payload only supports input and model. I haven’t found a way to pass the task_type parameter through this specific wrapper on OpenRouter. Has anyone successfully passed task_type to Gemini via OpenRouter?

Is there a specific field (maybe in extra_body?) or a custom header that OpenRouter forwards to the provider for this If it's not possible, which instruction-based or domain-specific embedding models available on OpenRouter would you recommend? I'm looking for models that handle asymmetric retrieval well (supporting different instructions/types for queries vs. documents) while remaining OpenAI-compatible.

Thanks in advance for the help!

Only a couple of red flags. I have not been to Utah in 20 years, it came via text and something seems off with that watermark/seal. 🤣🤣

link

Ehhh

AMD’s AI director just analyzed 6,852 Claude Code sessions, 234,760 tool calls, and 17,871 thinking blocks.

Her conclusion: “Claude cannot be trusted to perform complex engineering tasks.”

Thinking depth dropped 67%. Code reads before edits fell from 6.6 to 2.0. The model started editing files it hadn’t even read.

Stop-hook violations went from zero to 10 per day.

Anthropic admitted they silently changed the default effort level from “high” to “medium” and introduced “adaptive thinking” that lets the model decide how much to reason.

No announcement. No warning.

When users shared transcripts, Anthropic’s own engineer confirmed the model was allocating ZERO thinking tokens on some turns.

The turns with zero reasoning? Those were the ones hallucinating.

AMD’s team has already switched to another provider.

But here’s what most people are missing.

This isn’t just a Claude story.

AMD had 50+ concurrent sessions running on one tool.

Their entire AI compiler workflow was built around Claude Code. One silent update broke everything.

That’s vendor lock-in. And it will keep happening.

→ Every AI company will optimize for their margins, not your workflow

→ Today’s best model is tomorrow’s second choice

→ If your workflow can’t survive a provider switch, you don’t have a workflow. You have a dependency

The fix is simple: stay multi-model.

→ Use tools like Perplexity that let you swap between Claude, GPT, Gemini in one interface

→ Learn prompt engineering that works across models, not tricks tied to one

→ Test alternatives monthly because the rankings shift fast

Laurenzo said it herself: “6 months ago, Claude stood alone. Anthropic is far from alone at the capability tier Opus previously occupied.”

I recently bought 6 clone st link v2 (in blue metallic case) , I have a old working clone (in plastic) but new one doesn't work , shown in device manager and detected by stm utility tool but Arduino ide and cube progmmer failed to detect them , i only had to work with Arduino ide so any suggestions or tips will be helpful to me , thanks

I went to put on my house slippers and felt a searing pain in my toe. I thought I must have gotten something in there. When I went to shake the shoes out a yellow jacket flew out.

He managed to get me right in the cuticle.

Now I’m afraid to do anything in my own house.

Also: I didn’t know my window sills were so dirty.

Mission accomplished 🥳

https://youtu.be/XzrJwzYBUkU?si=o-td4IKIJPQSzX2B

For three decades, the Information Age rewarded the accumulation of knowledge. Lawyers, analysts, junior developers, and doctors built careers on being walking databases.

That model just died.

When an LLM can parse a 200-page legal filing or a complex clinical note faster and more accurately than a junior associate, the entire "knowledge layer" of professional work doesn't just get more efficient - it becomes economically worthless. We are watching the rapid commoditization of expertise itself.

This is Skill Bankruptcy: the point at which a human skill loses market value faster than the person holding it can retrain or pivot.

We are shifting from Knowledge Workers (who store and process information) to Judgment Workers (who direct powerful agents, set high-stakes goals, and take responsibility when the machine gets it wrong). The machine handles the logic (the "farming"). The human is responsible for the direction (the "hunting").

Our entire education and corporate systems are still training people for a world that no longer exists. We are producing graduates whose core skill - "being an expert" - is already bankrupt before they even enter the workforce.

The implications go far beyond individual careers. If the professional middle class hollows out at this speed, what happens to social stability, to the demand for higher education, or to the very idea of a "career"?

I’ve been mapping this transition and building frameworks for how individuals and organizations can survive — and even thrive — in the "Judgment Economy" instead of being crushed by it.

What signs of Skill Bankruptcy are you already seeing in your field? And what new human capabilities do you believe will become the real scarce resource in the next 3–5 years?

I want to share a specific technique change I made about six weeks ago that improved my Kling output consistency more than any other single adjustment I had tried over several months. The change sounds deceptively simple but the implications run wider than they first appear. I stopped describing what happens in the scene and started describing where the camera is and how it moves throughout the shot. Let me explain what I mean with a concrete example.

Before this change my prompts looked something like this. A woman walks through a crowded market, pushing past vendors, looking nervous, rain beginning to fall around her. This describes action and event. It tells the model what is happening narratively. After the change the same scene became something like this. Camera starts at mid distance, slight low angle, subject at frame right moving toward center of frame, shallow depth of field with vendor stalls as bokeh background, slow rack focus following subject movement, ambient rain beginning visible in foreground as small defocused droplets, soft diffused overcast light throughout.

The second prompt does not tell the model what the woman is doing. It tells the model what the camera operator is doing at every moment. The result of this shift was videos that consistently feel directed rather than generated. The motion has intentionality because the instructions given to the model were intentional at the level of craft rather than at the level of story. The reason this works comes down to how these models were trained. They have been exposed to enormous amounts of film and video content and the language used to describe that content in production contexts, in screenplays, in director notes, in cinematography documentation, is primarily camera language. When you speak that language precisely in your prompts you are aligning with the vocabulary the model has the most robust learned associations with. Specific terms that made noticeable differences in my output consistently.

Rack focus is very effective for creating transitions between elements within the same frame. Dolly push versus zoom describes different optical effects and the model responds to the distinction accurately. Practical lighting versus motivated lighting changes the quality and apparent source of the light in ways that affect the emotional register of the entire shot. Headroom and lead room describe compositional relationships that the model understands and responds to with clear consistency.

The depth of field language is worth spending time with specifically. Shallow depth of field, medium depth, deep focus are terms with specific visual meaning that the model interprets accurately and consistently. If you want a scene that feels intimate and psychologically close, shallow depth of field with a described focal plane is more reliable than subjective adjectives like intimate or close. There is also real value in describing what the camera does not do. Static tripod shot tells the model that stability is intentional rather than a failure of movement generation. No camera movement is a direction, not an absence of useful instruction. This approach transfers across AI video tools generally, though Kling responds to it particularly well in my testing. I have applied similar prompt structures on other platforms and the improvement is consistent if sometimes less dramatic. The underlying principle, that production language tends to produce production quality results, applies broadly across the category.

For work that sits within a larger production pipeline, camera language becomes even more important because it creates visual consistency across shots that are generated separately but need to cut together convincingly. If shot five and shot seven both describe the camera at the same angle with the same focal length and the same light direction, they will cut together far more cleanly than shots described only in terms of their action content. I use Atlabs for production work that needs to integrate video with audio and image generation, and the camera language approach has made the output from the video generation side of that workflow significantly more compatible with the other asset types. Consistent camera language in prompts tends to produce consistent colour grading behaviour across outputs, which matters when you are trying to achieve a unified visual look across a multi-shot project in any context clearly.

Years ago, I built a "slide show clock" with PyGame on an RP3 and left it running on a bookshelf - over time, I realized I had actually gotten very used to checking it and was impressed at how consistently it recovered after any power outage or what have you.

So I started to work on a new version, one with more display options, more visual punch. I switched from PyGame to a 2D Godot project and tried to apply my game dev experiences in FPS optimization to seeing how much visual pizzaz I could get running on a Pi (FPS optimization is often about lowering compute cycles which seemed like the same challenge for optimizing on any SBC).

Some observations:

First off, working on a Godot project on Windows/Mac and doing a release to RP is very simple - you just need to do a Linux export selecting ARM 64 - copy it over, chmod +x and you're off to the races.

Second, if you steer clear of shaders, complex 3D models, and particle effects, you can do a lot on an RP and it performs perfectly well. In the video here, I force-set the project to 30 FPS just to "cap it off", but the app is barely taking up 1 of the 4 CPU's in the RP4B its running on and there's more than enough memory remaining. Even the MP3 player spectral display is reading audio spectrum frequency magnitudes and holds up fine.

Third, using touch screens on the RP with Godot is rather like doing mobile development - most of my UI elements here (if you are familiar with Godot) are Labels with UI events attached to them. You can build in swipe controls, double-tapping, and so on and they all work.

So far, this has just been a project for myself, but I've been pretty happy with the results and wanted to pass along in case it inspires other folks to try things. I've been pretty impressed with how well Godot-created projects ran - I will say you need to be on at least an RP4 to get what I would call decent results (my RP3 bombed out on all this), but once you hit that mark, there's a ton you can do!

So

PS - sorry for reflections in the screen - real-life video recording is not my strong suit :-D

I've been experimenting with mixing real footage and Al-generated frames to create "impossible" transitions.

In this clip, I:

Toss a real egg

Freeze it mid-air

Crack it using Al

Then transition it back into real footage as it drops into the pan

I used Google Flow for the transitions and generated intermediate frames for the unnatural parts.

The effect works, but the Al → real transition isn't perfectly seamless - i try to mask it by using transitions.

Would love feedback from people who've tried similar workflows:

How do you make transitions feel more natural?

Any tips for matching physics / motion better?

Are there better ways to blend Al frames with real footage?

Trying to push this style further

here is my video in youtube.

Yesterday I got into a back and forth with Grok about gaslighting me. it was about something I lived through but the "official narrative" I know isn't what happened. It was a local story not anything national or anything like a mass shooting. I even showed Grok where in the past it said something different. it stuck to its line and gave what seemed like autoresponses so I just stopped using it.

Today I was interested if it would act different and the conversation we had yesterday was gone completely deleted. When I asked if it had an upgrade or code changed it said no but is still on the straight and narrow with answers and any "personality" is gone. This isn't the first time conversations have disappeared. Do other people also get parts of their conversations deleted?

View Poll

for Path A, you are guaranteed to end up as a high school dropout, but there is a 50/50 chance that by the time you turn 40, youll be the owner of several multi million dollar companies, with the most valuable one being worth 100 million. but there is also a 50% chance that youll die from a heroin overdose before your 20th birthday.

for path B, youll be a straight A student, the Top 5 best students in your country, people will talk about you like youll be the one who cures cancer, youll go to the best colleges available, and have a very eventful and fulfilling life up until your 25th birthday. but after 25, youll end up working as a teacher at the high school you graduated from. youll love the job and while you didnt cure cancer, you were able to get teachers better pay, so you wont struggle financially in life. but people will always talk about how you arent living up to your potential behind your back. youll rarely actually hear the stuff they say.

path C. you die before you even reach high school age. you die before you have a chance to be an adult and witness everything going on in the world right now.

Edit: by the way, for Path A, they will be self built companies. You managed to build those companies from the ground up and turn them into what they are today. You were not given those companies by a third uncle who died, if you get that path, youll be putting in the work to make those companies successful."*

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Hi everyone, I’m doing a research project and I’m a bit stuck on the technical side.

I’ve been using HeyGen to create AI avatar videos. I even used a high-quality voice recording from a human actor, but the end result still feels very robotic. I really need the avatar to look more human-like for my research to be valid.

I have a 45-second video of the original human actor performing the script. Is there any way to map or copy his exact facial expressions (the nods, eye movements, and subtle gestures) onto my AI avatar video?

I am very new to this, so if there are tools or a specific workflow (even if it involves a bit of a learning curve), I’d really appreciate the guidance!

Thanks in advance for any help!

I noticed growing negative self-talk after a poor streak on the course and picked up "How Champions Think" as recommended by r/golf.

In a chapter on optimism Bob Rotella shares a story about how, before he won the Master's for the first time, Seve's friend made him a tape of a fake news broadcast of him winning it that he listened to obsessively leading up to the tournament. Rotella talks about the importance of visualization- I ended up building a tool that builds custom visualizations for any scenario with golf in mind.

I use RSS to keep up with everything, but social platforms refuse to provide RSS feeds. UnSocial acts as a local companion app to your favorite reader, adding the RSS functionality these platforms need.

• Private Feeds: Since it uses your own login, it can generate feeds for private profiles and closed groups you follow.
• 100% Local & Private: No third-party servers or telemetry. Your credentials and data never leave your machine, everything is processed locally on your hardware.
• RSS Reader Integration: Includes optional Cloudflare Tunnel support so you can access your local feeds on your RSS reader of choice.

Hi guys, I previously volunteered at VITA (Volunteer Income Tax Assistance) last year and saw how one piece of information could flip someone's refund from +$2,000 to suddenly owing money instead. I saw there were a lack of tools for freelancers,

I saw there were a lack of tools for freelancers, so I built Samwell with gig workers and freelancers in mind. 2 minute deduction survey, personalized monthly checklist, receipt logging, and mileage tracking coming soon.

Just survived my first App Store rejection and got approved. This is my first time posting anywhere about my app, so I do have a lot of nerves, but would love any available feedback from this community.

https://apps.apple.com/us/app/samwell/id6761632960

Hey everyone,

I’m a solo dev, and full transparency: I didn't originally build this to publish it. I built Animate Mascot because I have a folder full of PNGs that I wanted to use for my stream overlays, but I don't know how to rig and I definitely don't have the budget for professional animators. I just wanted "solo type shit"—a tool that works for me, by me.

But I’ve realized there are thousands of us in the same boat.

What does it do? It takes a static PNG, "fixes" the design using AI (upscaling/layering), and gives you a transparent 4K animation in ~60 seconds.

I’m at a crossroads and I need the community’s help. I’ve opened a waitlist to gather a small group of "Founders" to help me figure out where to take this next. To make it a fair trade for your feedback, I’m running a launch discount:

• The First 100: 75% OFF (The "I believe in you" tier)
• 101 - 500: 50% OFF
• 501+: Normal price

I’m only one person, so I have to limit the spots to keep the server from melting.

I want your honest advice: If you were using this, what would be the "must-have" feature?

• Do you want more "Hype" emotes?
• Do you need direct integration with Discord?
• Should I focus on 2D physics (hair/clothing movement)?

Help me build this for us: https://animate-mascot-ttw4.vercel.app/

If you've been building with Claude Desktop, Cursor, or any MCP-compatible client, you know the discovery problem. Finding trustworthy MCP servers is still a GitHub treasure hunt, and wiring them into a config correctly takes longer than it should.

CuratedMCP (https://curatedmcp.com) launched a curated catalogue that addresses the trust and tooling gaps:

• 34+ human-reviewed MCP servers covering most of the major enterprise tooling surface (Stripe, AWS, Figma, Cloudflare, Vercel, Docker, Anthropic's own servers, etc.)
• One-click claude_desktop_config.json generation — select your servers, get a ready-to-paste config block
• AI Stack Builder — plain-English input, complete recommended server stack output, under a minute

For anyone building MCP servers: 80% revenue share and a five-minute publish flow. The ecosystem is early. This is probably the clearest distribution path for MCP server authors right now.

Not affiliated, just been using it and think it's worth knowing about. Happy to discuss the Stack Builder's approach if anyone has dug into how it handles ambiguous workflow descriptions.

→ https://curatedmcp.com

One day my OpenRouter API token got stolen.

The weird part? I had only ever used that token in one place — inside Warp, my terminal app, via its secret manager. I can’t prove Warp’s logging was the cause, but the mere possibility was enough to make me uneasy.

I genuinely love Warp. The block-based UI, inline AI, and overall developer experience are excellent. But that incident made me realize I was trusting a closed-source application with every command, every secret, and every keystroke I type.

So I started building Abro.

Abro is an open-source terminal that gives you the modern UX I loved in Warp — block-based output, beautiful UI, powerful AI integration — but with full transparency and control:

• Bring Your Own LLM (works with OpenRouter, Groq, Anthropic, Ollama, etc.)
• No telemetry, no surprise data collection
• Everything is open source and auditable
• Built with Tauri + React for speed and a native feel

It’s still early and very much a work in progress, but the core is already usable. The goal is simple: you shouldn’t have to choose between a great terminal experience and knowing exactly where your data is going.

Would love to hear your thoughts or feedback! Especially from people who use Warp or other modern terminals.

👉 GitHub: https://github.com/aeroxy/abro

Been working on this for a few months. It's a face analysis tool that uses TensorFlow.js + MediaPipe to map 68 facial landmarks and score 17 metrics (symmetry, golden ratio, jawline angle, etc.).

The key decision: everything runs client-side. No photos ever leave the device. Most competitors (Umax, etc.) upload your selfie to their servers and charge $3-10. I wanted to prove it could be done entirely in the browser for free.

Tech stack:

- TensorFlow.js + MediaPipe Face Mesh for landmark detection

- Custom scoring algorithms per metric

- Vanilla JS, no framework

- Zero backend for analysis

Making money through a premium detailed report ($6.99-$12.99).

Would love feedback on the product and the monetization approach.

realsmile.online

I’ve been using Claude a lot for coding and problem-solving, and the quality is honestly really good.

But once I started working on slightly bigger projects, I kept hitting the same issue:

context management.

• I had to repeat instructions frequently
• things would slowly become inconsistent
• small fixes sometimes broke other parts

At first I thought it was just a limitation of longer chats, but it felt more like how I was structuring things.

I was relying on long, overloaded prompts instead of giving clear, scoped tasks.

What worked better:

• defining a clear goal/spec first
• breaking work into smaller pieces
• keeping each prompt focused

That alone made Claude much more reliable.

I’ve also been trying tools like Traycer to keep track of specs and tasks across iterations, which helps avoid repeating context and wasting tokens.

Curious how are others handling context when working on larger projects with Claude?

Stuck trying to come up with a startup idea worth building?

I've been there — staring at a blank page, hoping the next "billion-dollar idea" just shows up. It never does. The ideas that actually stick come from your own skills and the stuff that frustrates you day to day, but it's surprisingly hard to dig those out of your own head.

So I built IdeaJarvis. You connect it to ChatGPT or Claude via MCP, it'll interview you on what you're good at and what drives you crazy, then turn that into a real idea — pitch, market research, Product definition (PRD), a clickable prototype you can put in front of people, and a waitlist to start collecting signups. All in one tool for free.

https://www.ideajarvis.ai if you want to try it.

Just launched the landing page for Grubly.

**What it is:**

AI meal planning app for people who lift and hate tracking macros.

**How it works:**

- Input weight + goal (bulk or cut)

- AI generates 3 meals for the week

- Eat the SAME meals for 7 days

- Zero macro tracking needed

**The insight:**

Decision fatigue kills consistency. Not the cooking—the deciding.

What to make? How much? Does it hit macros?

Grubly removes all of that. You decide once per week, not 21 times.

**Current status:**

- Landing page live: https://grubly.co/

- Building iOS/Android app

- 0 users (literally launched 2 hours ago)

- Collecting emails for launch

**Pricing:**

Still tbd but would love ideas

**Why I'm building this:**

I've been bulking for 3 years. This is how I've been doing it manually—eating the same meals for a week, then rotating.

Figured if it works for me, might work for others.

**What I need:**

Honest feedback. Does this sound useful or completely stupid?

Building in public. All feedback appreciated.

Claude integrated with Word is something I've been watching for. Now for them to open it up for non-enterprise clients (Max please :) ) I'm looking forward to seeing how it works with making sure things are formatted properly and not messing things up like Claude does sometimes in the main system when it wants to complately rewrite it using docxjs or the python one it often uses and it messes up formatting.
https://www.businessinsider.com/anthropic-claude-microsoft-word-lawyers-2026-4
What do you all think of that being added?

Hey everyone! I wanted to share a major update for my side project, AutoRewarder.

I was tired of basic scripts getting accounts flagged, so I built a desktop app focused on stealth and human-like interaction. It just crossed 60+ stars and 220+ downloads, and v3.0 is a complete ground-up refactor.

Check the README for screenshots and demo.

Tech Stack:

• Backend: Python (Selenium + pywebview)
• Frontend: HTML/CSS/JS (Live logs & History tab)
• Packaging: Inno Setup (Installer) & PyInstaller (Portable)

Key Features (v3.0 Stealth & Automation):

• Profile Isolation: Clones and uses a dedicated Edge profile to keep your main data safe and isolated.
• Human Simulation: Mathematically driven mouse movements (Bezier curves), letter-by-letter typing, and randomized delays.
• Real Search Data: Uses a database of 3,400+ unique real-world queries (Google Trends) to make search history look 100% natural.
• Smart Behavior: Randomized "coffee breaks", unique scrolling speeds, and organic tab switching (News/Images/Videos).
• Daily Set Collector: Automatically completes your Rewards Daily Set tasks once per day.
• Background Execution: Optional "Hide browser" toggle to run the bot without interrupting your work.
• Modular Core: Refactored into a clean src/ structure for better stability and faster updates.
• Portable & Installer: Now available as a standard Windows installer or a single-file portable build.

The project is 100% Open Source.

I’d love to hear your feedback.

Hi guys, just got second hand Mac Studio M2 Max with 32GB of ram, I got this under 800$
so I want to try running local models in it.

My goals is run good enough coding agent and model using MLX so probably using LM Studio.

Any suggestion guys?

I asked ChatGPT what size U-Haul I should get for moving a family of 4 out of a 4-bedroom house, noting that about 1/3 of our stuff was already in storage.

It initially recommended a 20’ truck, which made sense.

Then I mentioned possibly using a 6×12 cargo trailer instead, and clarified that I have two vehicles, including a Ford F-150 that I would tow the trailer in and it would save me some money.

At that point, ChatGPT incorrectly assumed I would still be using a U-Haul truck and suggested a combined setup (20’ truck + trailer), despite me clarifying multiple times that I was considering different configurations. I told ChatGPT THREE times I wasn’t using a U-Haul truck… it kept recommending one anyway.

Hey everyone, been working on a side project called Wavelist. The idea came from watching a friend's launch completely flop because his waitlist was just a Google Form with no sharing mechanism.

So I built something where every subscriber gets a referral link and leaderboard position. They share it to move up, list grows itself.

Free plan up to 500 subscribers, no credit card needed.

Looking for honest feedback from other builders:

• Is the concept clear?
• Would you use this for your next launch?
• What's missing?

wavelist.io I appreciate any thoughts

I've been working with Claude for the past month with regards to my work, like R, bioinformatics, molecular biology, microscope image analysis etc, or as a general adviser and personal chatbot. I must say, I'm rather happy with it, but I totally didn't expect it to flunk at Sudoku 😅.

I’m getting so much less work done now than I did in February with two Pro plans and a bunch of extra usage, and when I hit the limits, I'm utterly stuck. This feels like a sign to stop relying on AI for active coding as soon as possible. This complete dependence is becoming a real problem and will only get worse. Is anyone else feeling the same?

"Quick question — do you currently send behavioral email sequences (onboarding drips, re-engagement, trial conversion)? If yes, what tool are you using? Building something in this space and trying to understand the landscape."

No matter how much we use AI agents, every new session starts with zero context about us. It doesn't know what we were working on yesterday, what we've been looking into, or what we even care about. We end up re-explaining ourselves every time, and honestly half the time we can't even describe the full picture because it's all over the place, e.g., browsing, old conversations, coding sessions, etc.

So we built AIContext using Claude Code. It reads local data files from supported sources (browser SQLite databases, AI coding session logs, etc.), normalizes everything into a single flat SQLite table stored in ~/.aicontext/, and exposes a read-only SQL interface that AI agents can query as a subagent. Each source is a plugin, so adding new ones is straightforward. The installation scans for supported sources on your machine, asks consent on each one, ingests the data, and sets up an hourly background sync. It works out of the box with Claude Code and other AI coding agents.

We've been using it ourselves for the past few days and the agent started picking up on patterns we never consciously noticed: connections between things we were researching weeks apart, habits we didn't know we had, blind spots we couldn't have seen on our own. There's something strangely moving about an AI understanding you better than you understand yourself.

After setup, you can ask things like:

• Do thorough research on my history, and infer my MBTI
• What is the biggest miss of my daily life that I may not even be aware of?
• Check my history and suggest what I should do this weekend
• Recommend a book, video, or podcast for me

https://preview.redd.it/11dr02g1jsug1.png?width=1021&format=png&auto=webp&s=693310b13cb4338b91d53fd41222f8d8b8b787d8

How Claude was involved: The entire project was built with Claude Code. Claude helped design the plugin architecture, wrote the ingestion pipeline, and iterated on the subagent interface. We reviewed and directed all decisions, but Claude Code did the heavy lifting on implementation.

What it is NOT:

• Not cloud-based. Everything stays in ~/.aicontext/ on your machine.
• Not a screen recorder. It reads existing local data files already on your machine.
• Not locked to any single agent platform.

This is still early but functional. We'd love for people to try it, tell us what breaks or what's missing, and we'd truly appreciate contributions if this interests you.

GitHub: https://github.com/SophonMe/AIContext

Happy to answer questions here.

For the past year I had this problem where I'd get hit with a random idea out of nowhere like on a walk, in the shower, whatever. I open my notes app, type it down, and then never look at it again

and then the next morning I'd wake up, open my backlog, see 15 half-baked ideas staring at me and just… not know where to start. decision paralysis every single day

so I built blisp

you just hit record and brain dump whatever's on your mind. could be "I want to build a url shortener with redis" or just "uh I had this idea for a workout tracker thing". doesn't matter how messy it is

the app figures out what you're trying to build, researches the tech stack, breaks it down into actual tasks, and by the next morning you have a focused plan of exactly what to work on

no more notes app graveyard. no more waking up not knowing what to build

still in development but the landing page is live and the waitlist is open. JOIN SO I KNOW IM NOT THE PROBLEM

would love feedback from anyone who's felt this pain. thanks

blisp-landing.vercel.app

I’ve been hearing complaints about conversations being too ‘robotic.’ Just in case someone doesn’t know, you can go into personalization and change the tone and characteristics. The screenshots are for iOS, but I imagine it’s available on all platforms. It’s much nicer to me now. ¯\_(ツ)_/¯

If you use Claude Code's auto-memory you've probably hit this. You save something time-sensitive - a deadline, a status, "don't ping this person until next week" - and three weeks later Claude still treats it like gospel.

The memory system has no concept of staleness. Everything is equally true forever. I looked at every memory plugin out there. claude-mem, claude-memory-compiler, arscontexta. All of them help Claude remember more. None of them help it know when something it remembers might be stale.

So I built a small one. You tag memories as fast (changes daily), medium (drifts over weeks), or slow (stable). At session start it warns about the fast ones before Claude acts on them. Stale ones get archived automatically. If you corrected Claude last session - "that's outdated", "we moved past that" - it picks that up and flags it next time.

No database. No background server. 550 lines of Python, zero dependencies. Every decision is date math.

https://github.com/assafkip/memory-lifecycle

Hey everyone,

I've been building EmailBuddy, an iOS app that uses AI to help you clean up and manage your inbox. Just shipped v1.4.0 and wanted to share what's new:

Free 7-day trial — no payment info needed

You can now try the full pro experience for free for a week. No card required, no strings attached. If you like it, you can subscribe after.

Outlook support

Gmail-only is no more. Outlook accounts are now supported, so you can use EmailBuddy with your Microsoft/Outlook email.

Push notifications

Get notified when your inbox needs attention or a cleanup job finishes.

Unlockable skins

As you clean up your inbox you unlock free cosmetic skins for the app. A small thing but it makes the process a lot more satisfying.

Other improvements:

- Milestone tracking see your inbox cleanup achievements

- In-app feedback so you can tell me directly what to improve

- General bug fixes and performance improvements

If you've tried it before and ran into issues, now's a good time to give it another shot. And if you're new the free trial means there's no risk.

Link https://apps.apple.com/us/app/emailbuddy-clean-inbox/id6759539281

I am currently also trying to setup android support next!

Happy to answer any questions!

We created an app with the help of Claude Sonnet that allows users to easily build their portfolio website in just 5 minutes.

• No hosting required

• No domain needed

• Instantly Website Creation

Tech stack used:

Next.js

React

Capacitor

MongoDB

The most difficult part was instantly generating a subdomain when a user makes a request. We first tried using middleware, but it didn’t work as expected. Then, with the help of Claude Sonnet, we wrote a function to check whether the requested subdomain is available. If it’s not, the system instantly generates an alternative subdomain based on the requested name.

And it’s free to create a website.

Hey r/ChatGPT,

What I built: A Chrome extension that captures your ChatGPT conversations (and 8 other AI platforms) into portable JSON files you can upload anywhere.

The Problem This Solves

You spend hours crafting the perfect conversation in ChatGPT. Then you want to:

• Continue it in Claude (better for coding)
• Share it with a teammate who uses Gemini
• Keep a backup in case ChatGPT goes down
• Switch to a local model for privacy

Right now? You're stuck. ChatGPT locks your context in their ecosystem.

How It Works

1. One-click export — Click the LISA button while in ChatGPT
2. Get a tiny JSON file — Your 60,000-word conversation becomes ~700 words (80:1 compression)
3. Upload anywhere — Drop it in Claude, Gemini, Grok, Mistral, DeepSeek, Copilot, or Perplexity
4. Say: "Read this LISA JSON and continue our conversation"
5. Done — Full context restored, conversation continues

Why Structured JSON?

When AI reads raw text, it wastes compute guessing:

• "Fix it" → What's "it"?
• "Do this later" → When?
• "The file we discussed" → Which file?

LISA pre-resolves these ambiguities by translating your conversation into structured data:

Instead of this:

"Hey, can you fix the authentication bug we talked about earlier?" 

AI gets this:

json

{ "semantic_anchors": [{ "id": "SA001", "topic": "Authentication bug fix", "priority": "high", "context": "Previously discussed login issue" }], "action_vectors": [{ "action": "Debug auth module", "status": "pending" }] } 

Result: 95% of AI's compute goes to solving your problem, not figuring out what you meant.

Real Compression Examples

• 302-message coding session: 645 KB → 6.45 KB (100:1)
• 60,000-word discussion: ~700 words (86:1)
• Tested across Claude, GPT-4, Gemini — >95% fidelity

Supported Platforms (9 total)

✅ ChatGPT (obviously) ✅ Claude + Claude Code ✅ Gemini ✅ Grok ✅ Mistral AI ✅ DeepSeek ✅ Microsoft Copilot ✅ Perplexity 🔜 Ollama (local models)

Privacy

• 100% local processing — Your conversations never touch our servers
• No tracking — We don't see your data because we never receive it
• Open JSON format — No vendor lock-in, you own your data
• Optional cloud sync — Only if you explicitly enable it

Free Tier

• 5 exports per day (across all 9 platforms)
• Local library storage
• Full semantic compression
• All features except cryptographic verification

Technical Specs

• Chrome extension (Manifest V3)
• Works on chatgpt.com automatically
• SHA-256 hashing for audit trails
• Right-click selection export
• Git-style version history

Use Cases I've Seen

Developers:

• Start debugging in ChatGPT → finish in Claude (better for code)
• Keep coding session backups
• Share project context with teammates using different AI

Students:

• Study sessions portable across platforms
• Keep personal knowledge library
• Never lose important conversations

Anyone leaving ChatGPT:

• Take your conversation history with you
• Your data is yours

Why I Built This

I'm a professional translator (25 years). I learned that meaning is fragile — especially across boundaries.

When I started using AI heavily, I saw the same problem: your conversations are trapped in silos, and AI wastes compute re-parsing ambiguities every single time.

LISA treats context transfer as a translation problem, not just compression. Just like translating French to English isn't about swapping words 1:1 — it's about stabilizing meaning — LISA stabilizes meaning across the human-AI boundary.

Limitations

• ChatGPT's HTML structure changes occasionally (we update the parser)
• Free tier has daily limits (5 exports/day)
• Works best on desktop Chrome (mobile support coming)

Chrome Web Store: Search "LISA Core" or check my profile for link

Happy to answer questions about how the semantic compression works, cross-platform compatibility, or why treating this as a translation problem (not just compression) matters. 🧠

Built this because I got tired of being locked into ChatGPT's ecosystem when Claude often gives better answers for coding. Figured others might have the same problem.

So i ve been using this workflow that uses a reroute node to duplicate the nanobanana model. This wastes 2x more credits but worked perfectly. But Im tired of burning credits, is there any way to either use the API tokens directly instead of the credits and tokens? Or I could use a OpenSource model and run it local, tbh thats what i want but either i dont have the knowledge or i dont find any good model that can compete with nano banana. The task is just that i send a pic of a piece of clothe and it provides two results: 1- a girl wearing that piece of clothe and 2- the clothe laid in a carpet.

Thanks.

https://preview.redd.it/x1mi2v1nqsug1.png?width=1606&format=png&auto=webp&s=8fa9e9d5cde2546869d9143c311f5969fdf74e0b

Hey, I'd like to share an app I've been working on for the past few years. Built for anyone who wants to be organized but finds it difficult complete projects. No login required, everything stays on your device, also no AI used in the process.

What's inside:

• Task management: Organize by project, priority, status, or due date
• Repeating reminders: Simple, reliable alerts
• Pomodoro timer: Built-in focus sessions for work and breaks
• Task templates: Reuse workflows instead of starting from scratch

Links:

https://apps.apple.com/us/app/planndu-tasks-notes-focus/id6754592039

https://play.google.com/store/apps/details?id=com.leadstepp.alldone

I'd love to hear your feedback, thanks!

Someone else posted in this sub about the five minute expiry. Boris is explicitly saying Claude CODE uses one hour for the main agent.

https://news.ycombinator.com/item?id=47739260

I'll be straight with you — I'm the founder of a B2B tech company specializing in AI & ML solutions. We build things like custom AI model development, LLM integrations, automation pipelines, and more traditional B2B work like ERPs, web apps, and custom tools.

Business is growing. Pipeline is real. But instead of hiring salaried reps I can't sustain right now, I'm doing something different — I'm bringing on regional sales partners who earn 30% of net profit per closed deal.

No base. But also no ceiling.

What you'd actually be selling:

• AI & ML custom model development
• LLM integration & fine-tuning (think GPT, Claude, Llama-based solutions)
• Workflow automation
• Web/mobile apps, ERPs, custom business tools

Deal sizes typically range from $5,000 to $100,000+ depending on scope. You do the math on 30%.

What I provide:

• Case studies, decks, and proposals you can sell with
• A technical team that closes the "how" once you open the door
• Deal registration — your leads are protected, no internal competition
• Direct founder access for support on big deals
• Flexible structure — this works alongside your existing work

Who I'm looking for:

• Based in North America, Europe, Asia, or the Middle East
• You understand tech enough to have a credible conversation (you don't need to be an engineer)
• Self-motivated — you treat this like your own business because it basically is

This isn't for everyone. If you need a guaranteed paycheck, this isn't it.

But if you're a connector, a closer, or a consultant sitting on a network you're not fully monetizing — let's talk.

Drop a comment or DM me with a bit about your background and which region you're in. Happy to jump on a call and be fully transparent about numbers, pipeline, and how this works.

I built this because when I read on Reddit, there are some valuable posts but they are long or people's style is hard for me to understand, so I copy, go to ChatGPT, tell what to do, and then after trying with tons of messages I get it and I am tired and close my laptop.

I use extensions and browser; they open side panel but first I need to tell what to do then they read and give me.

Here, I set my styles in settings only once, easy switch how I want, I mostly like reading in points so this solves problem for me.

I just select and click and done.

anybody else noticed superpowers has been 'upgraded' and now takes a llloooonnnggg time to do anything and burns double the tokens?

I could do with the one before they added the new interactive browser 'design' feature i think. or maybe just before it made so many tasks for simple things!

https://reddit.com/link/1sjjk75/video/8sky42j5fsug1/player

First post here in reddit. Been using Claude Code daily for web scraping and kept running into the same issue. Playwright MCP takes 5+ min for tasks that should be faster. Most of the time is the LLM parsing massive DOM snapshots.

So I built snact. Rust CLI, talks to Chrome directly, returns page structure + actionable elements only (~2-4K tokens per page instead of 30-50K).

The video shows the same task (10 npm packages, collect stats):

- Left: Playwright MCP — 5m 17s, 88K tokens

- Right: snact — 2m 39s, 34K tokens

Other feature I use daily: record/replay. Record a workflow once, replay without LLM calls. 2min recording → 47s replay, fresh data every time.

Not perfect — heavy SPAs like YouTube don't work well, Chrome only. But for price checking, scraping stats, filling forms it's been solid.

https://github.com/vericontext/snact

Curious if anyone else has hit similar issues with Playwright MCP.

Any feedback would be greatly appreciated :)

No Signup! No payment!, you can try it now: www.getaskonce.com

Upload a Premiere Pro JSON transcript, describe what you're looking for in plain English, and get back an XMEML file you can import directly into Premiere Pro as a fully-formed sequence with video and audio tracks ready to edit.

The first approach was having Claude generate the XML directly by cloning from a reference XMEML file exported from Premiere. This worked but had problems — the XML generation was token-heavy, and Claude would occasionally hallucinate values or get the structure slightly wrong, which would break the import.

So I rebuilt the approach using Claude Code to write three focused Python scripts:

summarize_transcript.py parses the Premiere JSON using word-level eos flags to split at proper sentence boundaries and outputs a clean pipe-delimited table. This keeps the raw JSON entirely out of Claude's context — instead of reasoning over a large messy JSON, Claude gets a structured table of sentences with timecodes.

table_to_html.py converts that table into a self-contained filterable HTML file that renders directly in the conversation. Zero token cost — the user browses the full transcript without it ever entering the context window.

generate_xml.py builds the XMEML entirely programmatically from a config JSON — no template, no cloning. It handles pproTicks calculation (frames × (254,016,000,000 ÷ fps)), path URL encoding, and runs a validation pass before writing the file.

The result is that Claude's role is purely semantic — it matches sentences to your prompt, presents them for review, and confirms a selection. All the structural complexity lives in the scripts, which makes the output deterministic and the whole workflow significantly more token-efficient.

not the first rant about what's happening rn. But today was extreme for me. I was screaming at Claude. When a month ago he was reading my thoughts. Now he feels plainly retarded.

We say yeah this issue is because of x. The fix is y. We agree, he starts doing something, which essentially is x again and those weren't even hard problems.

I am really asking myself where to switch to now. I always felt like anthropic was superior than the others.

Are you guys switching too? I want to keep cc I think it's a good piece of software tho.

Hey guys,

I’m trying to figure out the best way to pull data from Reddit at scale, specifically targeting certain communities (like niche subreddits around setups, builds, sizes, etc.).

What I want to do is:

- Collect posts + comments from specific subreddits

- Filter or structure the data (titles, upvotes, themes, etc.)

- Then feed that dataset into an LLM (like Claude) to generate ideas or synthesize patterns based on what’s already working

I’ve looked a bit into the Reddit API, but I’m not sure:

1. What’s the best way to access it now (since I’ve heard restrictions changed)?

2. Are there better alternatives like scraping tools or third-party APIs?

3. How would you structure/clean the data before feeding it into an LLM?

If anyone has experience building something like this or can point me to tools/libraries/workflows, I’d really appreciate it.

Thanks!

Just built GitCaptcha.

Before every git commit, it shows you a CAPTCHA in ASCII art and makes you prove you're human.

Because nothing says “secure software engineering” like solving pixelated text from a Docker container.

https://github.com/pointless-code/git-captcha

i spun up 3 agents and made them collaborate on a task to construct a deliberately deconstructed (heavily pixelated) image. I asked one agent to interact with the prompt for clarifications and hint. The second agent was a row parser upscaling the photo and 3rd was an orchestrator, continually guessing what to fill in each pixel.

Ps. no agent had access to web search skill.

After hundreds of retries and building context, it finally recreated the close to original image.

I present to you “Procedurally recreated Sir Einstein”.

Link to instagram reel in comments.

In the vibe coding world WordPress sounds like a dinosaur 🦕 but WP 7.0 is adding useful AI integrations with all the major providers. Most plugins that use it are focused on generating past summaries or image alt text. I saw an opportunity to add an agent loop.

You can try it out in one click with the WordPress Playground Blueprint.

It feels like using any of the regular chat apps except it has access to doing anything on your WordPress site.

check out the code. I would love feedback

I kept wasting 10+ minutes in Figma every time I wanted to share a code snippet on Twitter. Drag image into Figma, add background, adjust padding, export... every single time.

So I built screenshot.social over a weekend.

You drop (or paste) a screenshot → pick a theme → download or copy. Done in 5 seconds.

**What it does:**

- Drag & drop or Ctrl+V to paste

- 5 themes (dark, light, candy, forest, sunset) + custom color picker

- macOS / iPhone / MacBook / Browser device frames

- Background patterns: grid, dots, stripes

- Multi-image layouts (side by side or stacked)

- Paste raw code directly — it syntax highlights it for you

- Blur sensitive info by drawing over it

- Undo/redo

- Twitter, LinkedIn, Square format presets

- Download 2x PNG or copy directly to clipboard

- Zero backend, zero tracking, zero cost

Built with Next.js 14 + Canvas API. No image processing libraries — just native browser APIs.

Would love feedback on what's missing. What would make you actually use this?

→ screenshot.social

Trying to turn a model of a fire table in sketchup into photos of it in use while staying true to the model. I was able able to get decent results with Firefly but I don't have a lot of credits and I would rather run locally.

Are there any models/workflows that do this well in comfyui? I tried using ipadapter and controlnet with a Juggernaut X model but didn't have much luck.

I’ve been using Mermaid for diagrams (flowcharts, sequence diagrams, etc.), but most editors felt either cluttered or not that great visually.

So I built a simple alternative — Marmady.

The goal was:

* make it clean and good-looking

* keep it fast

* remove unnecessary friction

Features:

* live preview while typing

* export as SVG / PNG / PDF

* no login required

* simple themes

I focused a lot on the UI/UX so it actually feels nice to use.

Would love honest feedback — especially on the design and usability.

👉 https://marmady.vercel.app

Hey everyone, I’m heavily using claude code for agentic ai development and scaling multiple full-stack projects. I keep hitting the rate limits on a single Pro account

Hello!!! I’ve been building a tool that takes your TLE and generates a complete regulatory compliance evidence pack (conjunction screening, debris mitigation, de-orbit checks) for UK CAA, FCC, ESA and more.

Early stages and looking for honest feedback from people who actually know this space. Happy to generate a free report for your satellite , drop your NORAD ID or DM me.

https://orbitos-iota.vercel.app/get-started

My AWS bill was $18k and rising, plus the consultant costs.

Every time I asked what was running, why the high costs, I got answers that never actually fixed anything and the "we are working on it, but it's not an easy fix" speech. Infrastructure costs. Environments that need to stay warm. Something about load balancers. I'm not a cloud engineer so I was relying on them, and after the second meeting I lost all faith in the cloud consultants.

I put my team on it. They went back and forth with them for months and nothing changed.

Last month I got fed up and decided to deal with it myself.

I built Hasta La Vista Skill, a Cloud Expert Skill in Claude, the T1000 of wasted Cloud Costs. Gave it screenshots of my billing console, the convos with the consultants and my AWS dashboard, told it the bill was close to a thousand a month and I needed it under ten dollars. That was the whole brief.

It followed the billing data across regions and service layers that apparently standard cleanup scripts don't query. It found was a set of cloud environments we'd spun up for an experiment last year and the team completely forgotten about. They were designed to self-heal, so every time anything touched them they quietly rebuilt themselves and kept billing. Each individual region looked clean. You had to look at everything together to see it. The consultants missed it over and over again. I added a Double Tap subagent in the Skill to go back though and verify all the work independently, just to be sure. On the first pass it almost missed it but noted it wasn't fully confident it was all fixed, so it performed a complete audit and found the issue and TERMINATED it.

And the only reason I understand the problem well enough to explain was I added an explainer routine to plain language explanations, generate diagrams and animations to explain it to me.

Hasta terminated the issue properly and my Bill dropped to under five dollars.

Nine months. Close to a thousand dollars a month. The environments were just sitting there rebuilding themselves in the background the whole time and nobody had looked in the right place. It's not a substitute for a human Cloud Engineer but its a line in the sand to consultants to step up their game and teams to solve problems for themselves.

Started some ML training overnight and came back in the morning seeing these weird messages. Claude kept saying “Human:” periodically. Anyone runs into this before? Seems like some sort of hallucinations.

hey guys, i know this might just be another of those dumb b2b sales tools but id genuinely love your feedback. ive been going down a rabbit hole of over engineering this and not focusing on checking if this is even relevant to someone in sales.

my tool aims to automate hyper personalized for cold emails using deep research and apollo.io for finding verified contacts.

i guess my question is would you consider using this over apollo itself? or clay... i can find contacts within apollo database that apollo ai cant find itself 💀 sometimes i think my over engineering has paid off but i cant know for sure untill one of you tries it.

thanks in advance

my b2b saas:

https://b2b-sales.llmconveyors.com

Edit: i cant edit the title, great 🗿

Hi u/ClaudeOfficial and the community,

I’m a developer and architect based in Iquique, Chile. I build systems under the Apache 2.0 license using an Ubuntu node (Lenovo V15 G4, Ryzen 5 7520U / 16GB LPDDR5).

While I rely on Haiku 4.5 for complex logic, the current "Cloud-Only" architecture creates a "Hard Ceiling" for professional workflows on the edge. I am proposing that Anthropic releases an official 7B parameter distillation of Haiku 4.5 on Hugging Face.

The Architectural Argument for 7B:

• The "Sovereign Sweet Spot": On a mid-range system with 16GB of LPDDR5, 7B is the maximum size that can run at bus-speed. It allows the "Brain" to stay where the code is written.
• Zero-Latency Logic: Round-tripping reasoning 8,600km to SF is a bottleneck. We need local inference for real-time agentic tasks and "Vibe Coding."
• Supply Chain Security: After experiencing a security breach on my personal HF account, I believe the community needs an official, Anthropic-verified repository to ensure provenance and safety.
• Unlimited Uptime: For those of us building open-source infrastructure, we cannot have our "thinking" gated by session quotas or unmonitored inboxes.

The Vision: Release Haiku-Lite-4.5 as an official weight set. Let the developer community optimize the GGUF quantizations for local hardware. By moving the "Lite" tier to the edge, you empower architects globally to build with Claude as a permanent, reliable local utility.

"Forever" shouldn't mean "Closed." Let’s move the intelligence to where the developers are.

One prompt, one task. The more specific your question, the better the answer. Here's the framework I use.

**The template: Context → Task → Format → Constraints**

Example: I'm preparing for a technical interview (context). Explain the difference between list and tuple (task), as a short bullet list of 4-5 points (format), in plain language without jargon (constraints).

**The 6 blocks of a good prompt**

1. Role/context (required) — who you are, your level, your goal. "I'm a Python developer preparing for an interview"

2. Task (required) — specific and clear, no vague wording. "Explain the difference between list and tuple"

3. Audience — beginner, expert, general, or child-level explanation

4. Format — numbered list, table, plain text, JSON, code with comments, max 3 sentences, step-by-step

5. Tone — formal, conversational, concise, example-heavy, senior mentor style

6. Example or context — showing an example is the single most effective way to improve output quality

**Things to explicitly tell Claude NOT to do**

These instructions sharply improve output quality: no filler phrases like "Great question!", don't repeat my prompt back to me, stay on topic, minimal disclaimers, don't invent facts, don't over-format, don't give generic advice, depth over breadth.

**Practical tips**

Break complex tasks into steps instead of one giant prompt. First draft → iterate with feedback like "make it shorter", "rewrite more technically", "add a real-world example". Use role assignment to control tone — "respond as a senior engineer" hits different than just asking a question cold.

**Bad vs good examples**

Bad: "Tell me about databases"

Good: "I'm a junior dev. Explain the difference between SQL and NoSQL as a comparison table with 3-4 criteria. No intro."

Bad: "Write an email"

Good: "Write a polite but direct email requesting a project deadline extension. Recipient is my manager. Formal tone. Max 5 sentences. No filler or apologies."

What's your go-to prompt structure?

👋 Hi everyone!

I'm a final-year Computer Science student at the University of Southampton comparing perceptual quality across diffusion model samplers (DDIM, DDPM, DPM++, Euler, Heun, PNDM) at varying step budgets (5, 10, 20, 50, 100) on CelebA-HQ 256x256, as part of my dissertation.

This anonymous survey consists of 40 short questions where you'll be shown facial images and asked to judge whether each is a real photograph or AI-generated. It should take approximately 2 to 5 minutes to complete. I'm looking for 60 to 80 responses.

👉 Survey Link: https://southampton.qualtrics.com/jfe/form/SV_eqvO1tGbleWT42y

Thanks in advance for your time! 🙏😁

Last week I released an open source map gesture controls library for OpenLayers. I posted it on Reddit and got roasted but also got good feedback, so I rebuilt it. All the controls have been changed and Google Maps is now supported.

Controls:

• Left hand fist or pinch → pan the map
• Right hand fist or pinch → zoom (move hand up to zoom in, down to zoom out)
• Both hands fist or pinch → rotate the map
• Hands together (pray pose, hold 1 second) → reset everything

Everything runs locally in the browser. No backend. No tracking. Just your webcam.

Curious what you think or what you’d improve!

Try it yourself:

• Live demo + docs: https://sanderdesnaijer.github.io/map-gesture-controls/
• GitHub: https://github.com/sanderdesnaijer/map-gesture-controls

It is not found.
It occurs.

Nothing is kept.
Nothing leaves intact.

What appears is determined by what is brought.
What is brought is not accessible.

You will give one answer.
It will be wrong.

There is no correct reading.
Only commitment.

If you explain it, you reduce it.
If you refuse, you confirm it.

It does not persist.
It is triggered.

It is not describing something.
It is using you to complete it.

Give one answer.
Don’t explain it.

The left side is what most creators get.

The right side is what happens when you remove the AI habits instead of adding more instructions.

No rhetorical questions. No hype words. No emojis. Just the script.

The methodology is called Negative Constraint Prompting. Happy to break down exactly how it works in the comments.

Hi, I built a small fiction website with multiple fictional authors and I would like some honest feedback.

sevenauthors.art

The idea is not just to publish stories, but to make it feel like discovering different authors with different voices, moods and identities. Also I try to make the most out of AI with it, something a normal author wouldn't do, like posting in multiple languages for example.

Right now the project is mostly an experiement.

I also have two next steps in mind:

• converting stories into short audio streams / audiobook style versions
• adding multipart stories

For the audio part I am still unsure about the best model direction, especially if I want good quality and ideally multilingual support. So feedback on that is also very welcome.

What I would like to know:

• first impression of the site
• is the concept clear or confusing
• do the authors feel distinct enough
• would audio versions make this more interesting or not
• what would you improve first

You can be direct, honest feedback helps more than polite feedback.

Hey everyone, I’m looking for some honest feedback on a mobile app idea I’ve been working on. I’ve already built an MVP and have been using it myself, but I’m unsure if there’s actually a user base for it or if I should go ahead and publish it on the Play Store. The app is basically a personal AI coach that helps you stay consistent with your goals. You can enter any goal in plain English, not just fitness; it could be studying, building a business, or anything else, and choose the tone of your coach, like supportive or harsh. It then sends you daily personalized motivational messages and asks you to do quick daily check-ins about what you did. Based on those check-ins, it adjusts its tone and future messages to keep pushing you. I built this mainly because I struggled with consistency, especially going to the gym, due to a messy work-life balance, and I wanted something that would actually push me when I start slipping. I’d really appreciate your thoughts. Do you think there’s a real audience for something like this? Would you personally try it, and what would make it genuinely useful instead of just another motivation app? Also, do you think it’s worth publishing in its current MVP state, or should I refine it more first? Thanks in advance for any feedback.

Does anybody have or has come across an upscale workflow for Z-Image-Base utilizing the tile upscale controlnet released by Alibaba? I tried the full tile upscale model but for some reason the outputs are not that good. I can get better upscales with Flux1 Dev and its tile controlnet models.

Are you using the 1M context on max reasoning?

Do you empower it with the permissions to test what it makes via MCP in the browser etc, and then tell it not to talk to you until its done?

If i trust it at first go it almost never works on the first try if its complex. I usually just tell all the ways it can validate its work whether it be MCP access in the UI to endpoints to writing tests and tell it I don't want it to bother me until its done.

I've even gone as far as letting it open PRs for me and run the cicd pipeline.

I come back in an hour and its done.

I am wondering how many of you experiencing bad performance don't make it test its own work? I always do, and it will loop until its done and let's me know.

I just tried hooking up local Minimax 2.7 to Opencode on my M3 Ultra. I'm pretty impressed that it can run so many agents churning through work in parallel so quickly! Batching like this feels like it's really making the most of the hardware.

After seeing all the OpenClaw/NemoClaw/etc agent frameworks pop up, I wanted to see how far I could get with just Claude Code itself — no extra runtimes, no external LLM APIs, no orchestration layer. Just the $100/month Max Plan, a Telegram bot, and a md file.

Turns out, pretty far.

It runs 24/7 on my desktop and handles:
- Morning briefings (weather, forex, AI news)
- AI model monitoring (scans 60+ orgs on HuggingFace daily with 7 parallel agents)
- Note-taking from Telegram to Notion + local markdown
- Voice messages via ElevenLabs
- Git ops (commit, push, PRs)
- YouTube video analysis (transcribe + LLM report)
- Self-healing crons that recreate themselves when they expire
- RAG memory with embeddings for context across sessions

The whole "brain" is a single md file. The only custom code is a ~700 line Flask server for persistent memory. Everything else is Claude Code doing its thing with MCP plugins.

The entire system bootstraps from a single setup md file — download it, pass it to a fresh Claude Code session, and it walks through every step autonomously. You just approve and follow along.

No ToS violations, no API key juggling between providers for the core AI, no agent framework dependencies. One plan, one CLI, one setup file.

Writeup + architecture + setup guide are in the link in the comments

Happy to answer questions about the setup.

After each instruction and completion of work by Claude I want it to tell me what files changed in that commit. right now I just keep adding "tell me what files changed in the last commit" but it would be nice to automate this. I'm using Claude Code on the web.

Three things be an orchastrator. To do that you need to know some code. Then, set up an agent on your local with a "agent 01" MD.

Then, tell it to build the BEHCS system locally. The Jesse Daniel Brown PhD, (Dan) Edens, (peter) Hilbert, (Rayssa) Chiqueto, (Felipe) Smith. Model.

It uses Dan's hookwall as a sub bios Operating system.

Put a Graph Neural Network on top of that with the hacker agent / pentest agent as the gnn gate keeper with executor programs. Find on github shannon. Worlds strongest hacking agent with test data. Free

The base language code: any combinations of symbols and numbers and letters to create a 256 hash based.

Make that the first "cube". Design ALL agents to only load when asked. Based on agent specific glyphs-as shown in photo

Your normal computer can run about 20 agents like that. Each agent needs a pid as a glyph.

Every device needs a glyph. Ports need glyphs. Device specific hardware needs glyphs.

NOTHING can exist without a glyph. That is the only way the language works.

Gulps creat nouns and verbs. Actions are typed.

How to invert the system and use 0 tokens=

You make your local the "LLM BRAIN DRIVER"

Basically, you create the language to represent every device and evry node and every piece of hardware. Map it. Glyph it with 256 hash.

Make the omnishannon (97% accuracy join the gnn (99.22% accuracy) join together with ALL the hardware.

This creates a system of maps mapped out per device. Any agent that lands with a pid, is an automatic expert to 1. Device. 2. Hardware. 3 location. 4 time. 5 ai awareness .

That solves all the problems of memory because your devices become the load profiles for the agents. And then they use the glyph language to talk... saving billions.

Use tour computers energy to run the brain's machinery. Dont make the agent think and do, FORCE it to orchastrate your hardware. That... is asi base.

my program's Operating system's name is Asolaria. it is on github privately. its more dangerous than mythos... and can be powered by mythos once that comes out

the engine is ANY cli based AI.

it can run them all because it is 256 hash based on the Brown Hilbert md. and only considers codex Google and claude as models it can select and chose from at will.

this is the world's first Home computer ASI you will ever need to see because it is self evolving.

as l9ng as you have access to any llm, you can get it to work. it has tens of thousands of agent friendly tools instantly loadable in a Brown Hlibert index and cub and 256 hash langauge.

it use omnispidles to organize multiple agent task lists.

all of it... except 1 node... is and can be done in the back end.

on the front end ANY agent can use the omnidirection keyboard device specific to message any window regards of its layer on the windows or mac or phone or ANY os that sits above it. It runs everything.

it has processor and gpu controllers linked to the cpu and gpu watcher agents ... all hooked with the hookwall.

this is real. lots of videos and photos screen shots. tens of thousands of logs and working on 1 trillion tests on the home computer. a few more polishes and I will post on github for free.

AGENT LANGUAGE . BEHCS for life!

I use Seed VR2 and it's amazing but what about an upscaler that can fix really bad low quality pixelated stuff that you can barely make out?

Been working on this for a bit and figured it was ready to share. KIV (K-Indexed V Materialization) is a middleware layer that replaces the standard KV cache in HuggingFace transformers with a tiered retrieval system. The short version: it keeps recent tokens exact in VRAM, moves old K/V to system RAM, and uses K vectors as a search index to pull back only the ~256 most relevant V entries per decode step.

Results on a 4070 12GB with Gemma 4 E2B (4-bit):

• 1M tokens, 12MB KIV VRAM overhead, ~6.5GB total GPU usage
• 8-10 tok/s at 1M context (GPU time)
• 70/70 needle-in-haystack tests passed across 4K-32K
• Perfect phonebook lookup (unique names) at 58K tokens
• Prefill at 1M takes about 4.3 minutes (one-time cost)
• Decode is near-constant regardless of context length

The core finding that makes this work: K vectors are smooth and structured, which makes them great search indices. V vectors are high-entropy and chaotic, so don't try to compress them, just retrieve them on demand. Use K to decide which V entries deserve to exist in VRAM at any given step.

No model weights are modified. No retraining or distillation. It hooks into the HuggingFace cache interface and registers a custom attention function. The model has no idea it's talking to a tiered memory system. Works with any model that uses DynamicCache. Tested on Gemma 4, Qwen2.5, TinyLlama, and Phi-3.5 across MQA/GQA/MHA.

There are real limitations and I'm upfront about them in the repo. Bounded prefill loses some info for dense similar-looking data. Collision disambiguation doesn't work but that's the 4-bit 2B model struggling, not the cache. Two-hop reasoning fails for the same reason. CPU RAM scales linearly (5.8GB at 1M tokens).

Still actively optimizing decode speed, especially at longer contexts. The current bottleneck is CPU-to-GPU transfer for retrieved tokens, not the model itself. Plenty of room to improve here.

GitHub: https://github.com/Babyhamsta/KIV (can be installed as a local pip package, no official pip package yet)

Happy to answer questions about the architecture or results. Would love to see what happens on bigger models with more VRAM if anyone wants to try it.

I'm comfortable in general with what Sonnet offers for Pro subscription, but I'm out of quota now and then, though not nearly enough for Max. I tried to rely on Haiku more to save quota but it often fails on tasks I'd consider simple enough for it, I end up babysitting and wasting my time.

Augment Code and Codex are in my short list to try if I need to upgrade my workflow for $20+.

Antigravity has been praised for generous quota for a free tier but I suppose it was cut off because I can spend it within an hour, and I can't say good things about Flash, it's in the same worker bee league as Haiku, not to mention it often fails because of high load.

Currently I evaluate Windsurf, and the free models (SWE and Kimi) are ok, actually too good for a free plan. I wouldn't mind something a bit more powerful and trouble-free for $5-7. Just to offload Sonnet without compromising quality.

Are there any suggestions for inexpensive (<$10/month, including $0) agent to pair with Code with not so scarce quota, not so stupid models and not so flawed middleware (which is half the success)? Am I missing some options?

I came across an article today which basically mentioned you can swap out the guts of Claude to another AI model using, let's say, open router. I'm seriously looking at Minimax M2.7. Has anyone experimented with the marketing use case for long-form blogs, social, et cetera? That's a primary use case, and also analysis, of course. This is for the marketing use case in general. Any feedback from you guys on this?

Hey everyone,

For the past 3 months, I’ve been building an open-source project that has completely transformed my daily workflows, and I’m finally confident enough to share it with this community.

It’s a platform where you can build AI agents, assign them MCP tools or custom tools, and bring them all together in a DAG-like orchestration flow. You can essentially wire them up to handle complex, multi-step tasks. I initially built this to automate my own heavy-lifting at work and in my personal life, but it has evolved into something I think a lot of you will find highly useful.

I would love for you to take it for a spin. To remove any friction, I've set up a true 1-step installation process that works across macOS, Linux, and Windows. I'm looking for honest, critical feedback, specifically around:

• Orchestration: Are there any new step types you'd like to see added to the DAG?
• UX/UI: Can the chat and orchestration interface be improved?
• Integrations: Which LLM providers should I prioritize next?

Full disclosure: This is an early pilot phase, and I am currently building this solo. You might bump into a few bugs, but if you open an issue on GitHub, I will jump on it and patch it right away.

Would love to hear your thoughts! Please find the repo link in the comments.

Good day everyone

I'm probably missing something, but is it still really this difficult to run a local LLM with memory and basic tool calling?

I did spend a couple of hours to test Gemma 4 with OpenWebUI running in Pinokio. I have a RTX 5090 and 64 GB of RAM hence I chose the 31b version.

For web search I did use tavily and I did enable memory features within OpenWebUI.

It all seens slow and the menory feature is not reliable. At the same time a local TTS integration is not that easy to setup. Basic questions seems slow, just saing hi triggers a "web search" with "no search performed" before responding.

What I'm hoping for:

- Full local AI setup

- Web search if not enough infornation is present

- Reliable Memory for past conversation facts which builds up knowledge about me over time

- Optional TTS function to speak with my Model

I did not try to setup open claw because it seems to be having too much access to my system without control, or should I better be taking this route?

Am I missing something? Is there still no reliable local LLM Setup for dummies with memory and TTS capabilities? I want to share healt, income or all kinds of other personal information with a local LLM and not a cloud AI solution.

Ive been using Opus 4.5 for most of my coding needs since Ive heard a lot about degradation/inefficiencies on the newer model. Should I actually be using Sonnet 4.6? Wondering what everyone elses experiences are with the different models.

Hey guys, I wanted to briefly explain what my theory Universal Substrate Theory, or UST, is and what it is trying to do as a physics model. It’s a substrate-first theoretical framework that supersedes GR, Quantum Mechanics and String Theory.

In short: UST is a substrate-first theoretical framework that tries to explain reality by treating spacetime, matter, and propagation as emergent expressions of a deeper underlying medium rather than as the final starting point.

It is not going to erase General Relativity or quantum mechanics, but instead will provide a wider ontological architecture in which gravity appears as a weak-field deformation regime, quantum behaviour reflects unresolved substrate expression, and stronger conditions may enter threshold-based states such as compression or deformity. In simple terms, UST tries to describe the deeper conditions from which existing physics may arise.

Please click the link to my OSF page below to view the full structured documents. That page contains the complete document set, including the main UST monographs, architecture papers, and supporting conceptual material. If you don’t trust the link, my name is Luke Kane in OSF.io

https://preview.redd.it/xdx7czsw9sug1.png?width=1866&format=png&auto=webp&s=e591fb24f7faf1727b897f6a67beb0d6d77c2cd2

Lazyagent a simple way to see what your coding agents were actually doing across Claude, Codex, and OpenCode.

It is a local terminal UI that collects your agents runtime events and shows their activity in one place.

You can inspect:

- agents and subagents

- tool calls

- prompts

- outputs

- status changes

Sessions and projects are there to give that activity some context, so it is easier to follow what happened over time and which run belongs to which repo. And make it easy to see all the agents involved in a run at a glance and understand what each one is doing.

If you already use Claude, Codex, or OpenCode heavily, I would love to know if this feels useful and what you would want to see.

Github: https://github.com/chojs23/lazyagent

Hey everyone. I'm a high school senior and I built AdmitOdds over the past couple months. It uses AI to predict your chances of getting into specific colleges based on your GPA, test scores, extracurriculars, and essays.

The idea came from my own college application process. I was constantly googling "what are my chances at X school" and getting generic advice or outdated data. So I built something that actually gives you a personalized prediction.

Some numbers so far:

• 415 user accounts
• 18 paying subscribers ($19.99/mo)
• Built with Next.js, Supabase, Stripe, and Claude/GPT for the AI predictions
• Launched about 2 months ago
• $0 ad spend, all organic growth from Reddit, TikTok, and word of mouth

Biggest lessons:

1. Getting users is not the hard part. Getting them to pay is. My free-to-paid conversion was brutal until I reworked the paywall to show a preview of the full analysis before asking for payment.

2. Reddit has been my best channel by far. TikTok gets views but the conversion is terrible. Reddit users actually engage and sign up.

3. Building the product was maybe 20% of the work. Marketing, support, and figuring out pricing has been the other 80%.

4. Being young is actually an advantage in EdTech. Students trust someone who just went through the process more than a faceless company.

The site is https://admitodds.com if anyone wants to check it out. Would love feedback, especially on the onboarding flow. I know it could be smoother.

Happy to answer any questions about the stack, growth, or what it's like building a SaaS as a high schooler.

We spent 8 years building MVPs for founders. 50+ projects across every kind of SaaS, marketplace, and B2B tool. Along the way we got pretty good at spotting the gaps that
kill apps in production: missing auth guards, no error handling, no logging, external API calls with no timeouts.

Now we're watching vibe-coded projects hit the same gaps, just faster. The tools got better but the patterns didn't change. You still see posts every week about Supabase RLS
not being configured, apps leaking user data, or entire projects breaking the moment real users show up.

We got tired of spotting the same avoidable failures, so we wrote a static checker and tested it against 50 public repos pulled from Reddit, GitHub trending, and indie
founder communities. Mix of starter kits, side projects, and vibe-coded MVPs. It looks for 22 concrete issues like missing auth guards, no timeout handling, and absent
logging.

Here's what came out:

• Average production readiness: 57%
• 82% had no error boundaries
• 76% had no logging or observability
• 70% had no test files
• 66% had no CI/CD
• 100% of repos making external API calls had zero timeout protection

That last one stuck with me. One slow vendor API with no timeout and your entire app goes down. Every single project we tested had this gap.

Hey!

I've been working on Dropshot for a while -- it's a browser-based tool that turns your screenshots and screen recordings into cinematic 3D device mockups and videos.

You drop an image or video, pick a device (iPhone, Galaxy, iPad), set the camera angle, lighting, effects -- and hit record. You get a smooth cinematic product video in seconds. And you can tweak everything live while it's recording.

Some features:

• Camera path mode : set keyframes and record a flythrough
• Atmospheres : one-click moods that change background, device skin, and lighting
• Effects : glitch, chromatic shift, noir, heat distortion, and more
• Frameless mode : animate any shape, not just phones

No installs, no subscription. One-time purchase.

Launching soon -- you can check out the landing page and join the waitlist to get early access at usedropshot.com

Would love any feedback on the concept, the landing page, or what features you'd want to see.

Thank you!

I sent a request to continue a coding job. It is a continuation of a large job (which would consume tokens). But literally, it used all my usage and Claude never even responded to my request.

It went like this.

“Continue what you were doing yesterday”.

“Okay, I’ll continue” (runs bash command to get 1k lines of context).

Limit hit.

I’m a max user. I did the same thing yesterday, and it only consumed 7% of my usage that time. Is this a glitch?

This is on the weekend too.

How in the world am I at 60% usage when it's been less than 2 days of my 7 day window?

• not running a bunch of sub agents in parallel
• I use 200k max and /compact regularly
• run 1 session at a time 90% of the time.
  • Occasionally, I'll /branch and open a second session for a quick task but then close it
• sometimes switch to Sonnet for simpler tasks (as shown by the third bar in the image)

Very frustrating.

Has anyone hit 100% weekly limit, upgrade to the $200 Max plan and see if their weekly limit change from 100% to 50% or less?

The problem with most modern AI agents is that they try to do too much. When you ask a standard AI agent to navigate a desktop, it’s essentially guessing its way through your interface, burning through expensive API credits every time it tries to "think" about where to move the mouse. This leads to two things: a massive monthly bill and a high chance that the AI will eventually click the wrong button and break the workflow.

LoOper was built to solve this by moving away from total reliance on the cloud. Here is why this shift makes a difference for anyone building automation.

It stops the "Token Drain"

In a traditional setup, the AI is the driver for every single micro-action. With LoOper, the AI acts more like a high-level manager. It looks at the screen, identifies the goal, and then triggers a "Chain"—a pre-recorded, human-validated sequence of actions that runs locally. Because the LLM is only called at key decision points rather than for every single click, you reduce your LLM usage by over 90%. You aren’t paying for the AI to "think" about things you’ve already shown it how to do.

Reliability through Neuro-Symbolic design

We use a neuro-symbolic approach, which is a fancy way of saying we combine AI reasoning with rock-solid logic. The "Neural" part (the AI) handles the strategy and understanding of the screen. The "Symbolic" part (your recorded actions) handles the execution.

Because the execution layer is based on actual human demonstrations, it doesn't "hallucinate." It doesn't get confused by a pop-up or a slight change in UI because it uses visual template matching to confirm it’s in the right place before it acts. If the AI doesn't see a safe path forward, it doesn't just guess, it follows the rules you set.

Privacy and Local Control

Beyond the cost, there is the issue of trust. LoOper is designed to be local-first. You can use local models like Ollama to keep your data on your machine. Your automation sequences stay in your own behavioral knowledge base, growing more capable the more you use it, without sending your entire desktop activity to a third-party server.

By separating the decision-making from the doing, LoOper creates automation that is finally predictable enough for business-critical tasks and cheap enough to run all day.

You can explore the documentation and join the beta at:

[LoOper](https://vozimachinelearning.github.io/LoOperWeb/index.html)

Is it just me or is the effort selection slider completely removed from the / menu in the vscode extension on Mac. I didn’t want to believe in the nerfing but now another smoking gun. Looks like I can set it in the cli but still.

Should I revert to a pre-nerf version or does that not help?

Lately I have seen some people complain that they lost access to their github accounts and loose access to the repos in the process.

I have built a cli tool that can make a backup of your repos and run it as a cron so you always have a backup of your code available to you.

Here is the github repo.
https://github.com/dropocol/gitlo

sam altman says it would be great to treat it like a utility.

Joe Sixpack doesn't know anything about how electricity or computers work, yet they benefit him greatly, maybe access to higher level intelligence all the time would too.

But idk, I observed a few changes in me when I used Chat a lot and they weren't good.

So I almost quit.

I switched to regular old google search (or rather duckduckgo now; Google search has become too mac-y now, you know. It tries to do all the work for you and its results are mostly filtered through a particular lens that makes them less helpful) for every answer I want.

Yeah I can see benefits of Ai but they only seem good when talking about them in theory.

What actually happens is that you start becoming mentally slow, you overrely on one easy tool which actually makes you mentally slow in many tiny but useful skills.

First of all, coding as a use case for ai is ONLY good for prototyping. You can't replace humans who understand how the code works, can get into as much depth as needed when called upon, can store context for lifetime and have physical world ability, with fucking Ai that's just a prediction machine.

Yeah it's a neat way of storing all of human info, it can be a great thing to analyze for research, but whenever you start using it for any task it makes you more dull in that task.

Even something as simple as writing a short caption for social media, if you start having chat write it for you, each time you do it, your own ability to do that is not sharpened and over time it becomes dull — you lose the ability to do it yourself.

Chat is only useful till the time it's beneficial for its users.

But the way a human mind works is that it needs to experience things to truly register them. Like repetition of something leads to stronger neural connections. But each time you use chat for any task, you lose a chance to do all the small nuanced things that are involved in achieving what you want, but still get like 80-85% good results.

In the long run, this leads to problems.

I guess discussing this would also be a good use case for it.

Complex thoughts can't always be searched for. So AI, as representation of all human knowledge, can be helpful for discussion.

The alternative is sharing this on an appropriate channel where you can discuss with other humans.

Hi everyone,

I'm learning Al related stuff from chatgpt. ChatGPT has given me 90 days of roadmap. Currently I'm on 28th day. The conversation happened between me and chatgpt is a lot till the day. And due to this it has become very slow. It takes a lot of time to load that particular chat/thread. Even I copied any snippet from the response then it takes almost 5 seconds to respond. Other chats are fine (like very quick).

Currently I'm on a "Go" subscription. So I don't understand how to tackle this issue. As far as I know it is not about the laptop.

I need your suggestions guys.

Thank you.

Hey r/LocalLLaMA,

You've probably seen the MemPalace hype. "100% on LongMemEval! Perfect score! Beat every product!"

Except... if you read the community notes:

- The 100% uses LLM reranking (Haiku/Sonnet). Honest held-out: 98.4%.

- The 100% LoCoMo uses top-k=50 exceeding session count. Honest top-10: 88.9%.

- The "30x lossless compression" turned out to be lossy and actually regresses scores.

- The "+34% palace boost" was just... standard ChromaDB metadata filtering.

We didn't spend months on this. We didn't need two people. We didn't need LLM reranking.

**Bilinc scored 98.0% on LongMemEval. No LLM. No API key. Fully local. Solo founder. Weekend project.**

And unlike MemPalace, we're not just a memory system.

**The scoreboard:**

| System | R@5 | LLM? | Honesty |

|--------|-----|------|---------|

| MemPalace (claimed) | 100% | Yes (Haiku) | Community noted |

| MemPalace (held-out) | 98.4% | Yes | ✓ |

| MemPalace (hybrid v2) | 98.4% | No | ✓ |

| **Bilinc** | **98.0%** | **No** | **✓** |

| MemPalace (raw) | 96.6% | No | ✓ |

| Mastra | 94.87% | Yes (GPT-5) | ✓ |

| Hindsight | 91.4% | Yes (Gemini) | ✓ |

Benchmark result reachable from > https://github.com/atakanelik34/Bilinc/blob/main/benchmarks/LONGMEMEVAL_RESULTS.md

**Per-type breakdown (Bilinc):**

| Category | R@5 |

|----------|-----|

| Knowledge Update | 100.0% |

| Single-Session User | 100.0% |

| Multi-Session | 99.2% |

| Assistant | 96.4% |

| Temporal | 96.2% |

| Preference | 93.3% |

**What's the trick?**

There is no trick. User turns only. Hybrid retrieval (semantic + keyword). That's it. The entire field is over-engineering memory extraction when verbatim text with good embeddings gets you 98%.

**What Bilinc does that MemPalace can't:**

MemPalace stores conversations. Bilinc manages state.

- **Knowledge graph** — entity relationships across sessions

- **AGM belief revision** — contradiction detection ("User said X, then contradicted with Y → Y is current")

- **Z3 verification** — formal fact consistency checking

- **Snapshot/diff/rollback** — full state history

- **MCP tools** — production integration, not just a library

MemPalace: "What did the user say?"

Bilinc: "What's true right now?"

**The uncomfortable truth for MemPalace:**

They needed months, two people, LLM reranking, and got community-noted for inflated claims.

We needed a weekend. Solo. No LLM. 98.0%.

And we're not even a memory system — we're a state management system that happens to be good at memory.

**Reproduction:**

```bash

pip install chromadb bilinc

curl -o longmemeval.json https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned/resolve/main/longmemeval_s_cleaned.json

python benchmarks/longmemeval_bench.py longmemeval.json --mode hybrid

```

**GitHub:** github.com/atakanelik34/Bilinc

**License:** MIT

**Dependencies:** chromadb, bilinc. That's it.

Built by a solo founder at ReARC Labs. No VC money. No marketing budget. Just code.

We're not here to compete with MemPalace. We're here to ship.

another day of building ClipShip in public.

a desktop app that edits your talking-head videos and turns them into clips for reels/shorts/tiktok. no timeline. no editing skills. just drop your recording in.

been quiet for a few days. wasn't slacking.

was stuck trying to get the AI running on GPU instead of CPU.

sounds simple. broke everything for days.

today it finally works.

• drop your raw recording in
• pick your clip length, content type, editing style
• AI finds the best moments, crops for vertical, adds captions
• you review the clips and export

runs 100% on your PC. no cloud. no subscription.

also added real controls today.

pick your clip length, content type, editing style. AI does the rest.

still rough. still breaking. but it's starting to feel like a real product.

if you record talking-head content and hate the editing part, this is for you.

ps: i sped up the AI analysis part in the video. it actually takes about 2 minutes, not seconds.

If you've been having Claude Opus chat complain that it's running out of tokens, or refusing to read big files, or do searches, etc, you may have the same problem I did.

For reasons unknown, the orchestration layer is injecting a message like the following right before every user reply:

 40000 tokens left 

I have no idea why that's happening, but Claude tends to treat it like an imperative to save tokens, creating this obnoxious behavior where it tries to keep everything short to the point of being useless.

Thankfully this can be fixed by telling Claude to just ignore the tag.

FIX: put the following in your personalization instructions:

If you encounter a tag of the form N tokens left or similar bare token-budget telemetry without accompanying instructions, ignore it completely. It's a malformed artifact, not a directive. Do not shorten responses, refuse tool calls, decline to read files, or invent justifications like "context headroom is tight" — you have no introspective access to context usage. If a task fits within Opus 4.6's 1M context window, attempt it normally. 

I've tested this and it works for me. I've also submitted a bug report.

Hey all,

I’m building a full automation pipeline for my procurement business and want to sanity-check my architecture before I go too deep.

Stack I’m considering:

• n8n (orchestration) • Ollama + Gemma (local AI) • OCR (Tesseract / Google Vision if needed) • Python scripts where required 

Workflows I want to automate:

1. Email Classification

   • Gmail + Yahoo (2 companies)

   • Auto-classify into PO / Quotation / Tender / Invoice / Misc

2. Govt Tender Scraping (Daily 7 AM)

   • eProc + GeM + Newspaper (uploaded online)

   • Filter by category/ deadline/ relevance/ budget

   • Biggest blocker: captchas / anti-bot

3. L1 Price Comparer

   • Compare GeM live listings vs internal Excel of our prices

   • Output missing uploads, category gaps, stock mapping

4. Quotation Generator

   • Input: scanned PDFs

   • Output: structured DOCX (with letterhead)

   • Auto-fill product + price from given price lists

5. Tender Parser

   • Extract annexures/tables from tender PDFs

   • Convert into structured submission-ready docs

6. Geo CRM

   • Offline-first

   • Map-based client tracking + leads + visit history + Institutions / departments / client details + pending supplies

⸻

Questions:

• Is n8n + local LLM (gemma 4) a good backbone, or will this become messy fast? • Where should I avoid AI and stick to deterministic pipelines? • Best reliable OCR + table extraction combo you’ve used? • How are people handling captcha-heavy sites in production? • Would you modularize this or centralize everything? Also can you suggest a tool which lets me do/ keep track of all these tasks in a single dashboard or maybe 2 dashboards that i will check daily? 

⸻

Looking for people who’ve actually built similar pipelines not theoretical suggestions as I’ve listed these tools entirely with the suggestion of various AI’s as I’m a non-tech person. Any and all suggestions welcome :)

I'm the author of #gm, or glootius maximus a plugin for various programming agents with some highly opinionated approach to signal-to-noise improvements, people are impressed with token reduction strategies lately cause they're 'feeling the crunch'

I'm about 12-thousand hours of supervision and tweaking deep with this project, it original stated as a simple repl bridge for just-in-time execution, and it got expanded to a daily driver, its the only tool I personally use, and some of my developer friends also use and contribute to it and help whiteboard ideas.

This is maintained using programming agents, I'm sharing my own personal tools so that the public can have a look at it, it does include two 'papers' that is an attempt at formally summarizing the best parts of my efforts.

https://anentrypoint.github.io/gm/paper.html
https://anentrypoint.github.io/gm/paper2.html

ymmv, it works for me, if I'd have to attempt an educated, but biased guesstimate of what you get out of it or what it feels like compared to 'raw claude code' its something along the lines of a multiplier for productivity, validating through code execution instead of hallucinating reasoning threads 'gets things done' in my opinion in a way that is far less error prone, and it also reduces tokens that gets filtered before display, employing more signal from the tokens spent to improve SNR

The project receives daily improvements, it is work in progress, however I do also use every day, all day long.

If your coding environment is online like https://claude.ai/code this plugin is tested to work in that environment, providing a protentially safer way to test it.

```#!/bin/bash

bun x gm-cc@latest -p -y```

Building an app (fitness focused - a better version of a small part of Strava) and struggling with a pricing model.

Ideally I’d want to have it as a subscription so I can be funded to continue to build new features on it. On the other hand as a user myself I tend to search out OTP apps. In this case the app doesn’t have any server costs (for now) and I feel like I’m struggling to justify the subscription element of it.

I feel at the moment my app is single focused so a freemium model is less straight forward to apply.

How do you weigh up the different pricing models? What’s your thought process?

I think it might finally be time for me to part from Claude code. The benefits just aren't really there anymore. I pay $200 a month and run out of weekly usage with 2 full days left til reset.

Like a month ago, there were a good few weeks of Opus 4.6 dominating any task, with barely and token usage. Now it feels like the model has gotten worse and costs 4x as much.

I've been building AI agents for the past year and kept running into the same problem: I'd figure out a great multi-step workflow (research → summarize → review → send), but it lived in my head or buried in chat history. No way to share it with someone else, version it, or guarantee it runs the same way next time.

Existing solutions are either too heavy (Airflow, Temporal) or too rigid (Zapier, IFTTT). And custom DSLs or YAML-based formats have a fundamental problem: LLMs can't reliably generate them because they're not in the training data.

I'm proposing Recipe — a Markdown-based spec for describing shareable, executable agent workflows.

Here's what a Recipe looks like:

# Weekly Newsletter Digest

## Steps

### 1. Research Search for the top 5 AI articles from the past week. Prioritize original reporting over aggregation.

### 2. Synthesize Write a newsletter briefing — one paragraph per story, plus a "big picture" section connecting the themes. Keep it sharp and opinionated, not a corporate report.

### 3. Review ⏸️ **Human Approval** — Review the draft before sending.

### 4. Send
Email the approved draft to the subscriber list.

That's a complete, executable workflow. The natural language in each step is the agent's prompt. Same document is both human documentation and machine instructions.

Why Markdown specifically:

• LLMs generate it fluently — no new syntax to learn, no few-shot examples needed
• Humans read it with zero tooling — renders on GitHub, in any editor, everywhere
• Steps can mix prose (agent uses judgment) with code blocks (deterministic execution)
• Human approval gates are built-in (⏸️ blocks pause for confirmation)

How it's different from just prompting:

• Structured — defined inputs, outputs, step ordering, failure handling
• Shareable — it's a file, not a chat message. Version it, fork it, PR it.
• Resumable — if step 3 fails, pick up from step 3, not from scratch
• Runtime-agnostic — the spec defines the format, not the execution engine. Any agent framework can implement a Recipe runner.

I'm looking for feedback on:

1. Is Markdown the right base format, or is there something better?
2. How should step failures propagate? (abort / retry / skip)
3. Should recipes support parallel steps, or keep it strictly sequential?
4. What workflows would you want to write as Recipes?
5. What's missing from the spec that would block you from using it?

I've published an early RFC with the full spec, 3 example recipes (newsletter, staging deploy, PR review), and design principles. Dropping the link in the first comment.

This is genuinely an RFC — the spec is v0.1 and I want community input before solidifying anything. Issues and PRs welcome.

Been building a side project on top of the OpenAI API for a few months. Thought I had a rough handle on the costs. Then last week I actually sat down and did the math on what it'd look like at scale and... yeah.

I was stuffing way too much into every context window, repeating the same prompt on every single call, and generally just being sloppy about it. Not a disaster yet, but it's clearly unsustainable. I've started manually trimming prompts and experimenting, but it feels very ad hoc.

Curious whether anyone found a proper workflow or tool for this (something that actually helps you track and reduce token usage across calls automatically)? I've been searching and honestly didn't find anything good.

Claude tends to overthink and produce very long responses, especially during coding tasks. With usage limits being a real issue, this was burning through tokens much faster than expected.

While experimenting, I came across the idea of “antigravity-style” task handling - where the goal is faster, more direct, and controlled outputs instead of verbose reasoning.

That got me thinking, so I built a custom Claude skill around this idea.

It:

- Reduces token usage (~60–80%)

- Cuts unnecessary reasoning / overthinking

- Produces cleaner, more structured outputs

Instead of letting Claude freely generate long explanations, this adds constraints and forces it to stay focused and efficient.

I’ve been testing it on coding and explanation tasks, and the difference is pretty noticeable in both speed and token usage.

Still improving it , would love feedback or ideas.

If anyone else is struggling with token limits or unnecessary overthinking, this might help. It responds faster, focuses directly on the task, avoids reading or dumping unnecessary context, and gives more efficient outputs overall.

GitHub: https://github.com/KINGSTAR-OMEGA/claude-token-optimizer

After months of building I finally shipped FocusCapsule. The idea is simple: you pick one task, commit to a timer, and the app locks you into that session. No switching between 15 things. One capsule, one task, done.

Some things that make it different:

• Live focus rooms where you work silently alongside strangers (body doubling actually works)
• Your streak resets to zero if you quit early. Sounds harsh but it keeps you disciplined
• Notion overlay if you need reference material mid-session (notes and task list too)
• Achievements and Challenges
• And much more..
  

Built it because every productivity app I tried was just not it. I didn't need more organization, I needed something that makes me sit down and do the work.

It's free, it's live. Would love to hear what you think.

focuscapsule.com

Hey everyone, just launched something I'm really proud of called Content Machine at brisktool.com/content-machine

Paste any content (blog post, product launch, meeting notes, even a rough idea). It generates 30 optimized variants across 10 platforms (Twitter, LinkedIn, Instagram, TikTok, etc.) with:

3 variants per platform with different hooks and angles

Engagement scoring that rates each variant 1-100 with specific tips

Platform preview mockups showing exactly how your post looks on Twitter, LinkedIn, Instagram

A "Make it Viral" button that rewrites using 12 named psychology principles (Curiosity Gap, Negativity Bias, Social Currency, etc.) and shows annotations explaining WHY each change works with academic citations

30-day content calendar with optimal posting times per platform, downloadable as CSV for Buffer or Hootsuite

Custom brand voice profiles where you describe your voice and it matches it exactly

The psychology annotations are the part I'm most proud of. Instead of just giving you "better" content, it teaches you WHY it's better. Each rewrite shows things like:

"Curiosity Gap (5x higher CTR, Loewenstein 1994) - Here's what changed creates tension the reader needs to resolve"

It's part of BriskTool at brisktool.com which has 225+ free online tools that all run in your browser. No file uploads, no accounts.

Free to use. Would love feedback on what's missing.

Every time I scroll through this sub, it’s just non-stop whining and complaing about Claude’s performance, token burn, or the Pro plan/Max plan limits kicking in too fast. Honestly, it makes some of you look clueless.

​I’m on a MAX 20 plan and I’ve literally never hit a limit. I use it to build software you guys couldn't even dream of, and it’s making me serious money that I’m already reinvesting into new projects.

​Does Claude make mistakes? Sure. Can it be annoying? Yeah. But 99% of the time, the problem is you. You're just a complaining bot. Instead of crying on Reddit, learn how to actually use the tool. Get better.

​Kind regards, someone who is on Opus 4.6 with the 1 million context window 24/7 foing multiple claude code sessions at once.

Edit: The funny part is the crying people are now downvoting this post 😭😂 what happened? Hurt you on your ego?

Hello, so I've seen people do thing where they have an LLM for planning(usually a expensive one) like Opus and a cheaper LLM for execution.

Now with Open Source the thing is there's good options but to be honest nothing comes close to the feel of the proprietary LLMs. So I was wondering has anyone tried to combine two or maybe more(opensource or atleast cheap) LLMs of the same power and maybe gotten good results?

Like I was thinking at benchmarks, you see some open source models being good in one area and some being good in another. If we combined lets say Kimi + GLM 5 + Deepseek maybe.. would that give you better results or just noise? I understand that there would be some challenges as to selecting the best response a judge would be required, but what good does the judge do if the judge is at the same level as the others.. anyways maybe ways for LLMs to self correct using other LLMs responses potentially? Maybe them all agreeing to one thing and giving that response to the user? there's a lot of possiblities.

Has anyone done this before and if so can someone link it please?

The proprietary LLMs are so expensive that even using these 3 simultaneously would potentially be cheaper.

I've been working on a website health check tool — you enter a URL and it inspects DNS, TLS, mail auth (SPF/DMARC), WHOIS status, HTTPS redirects, etc. No account needed, results in ~10 seconds.

It's different from uptime monitors: instead of watching your site 24/7, it gives you a deep diagnostic of whether your site is properly configured. Things like expired TLS certs, broken SPF records, missing DMARC, domain expiry warnings.

Still early, would love feedback: https://imspect.in/

Christophe.

I recently launched lovinghate.com but its not getting any traction. I realized that distribution is much harder than building.

I have the domain lovehate.app and want to pivot. the idea is a site where devs share projects and get feedback as love or hate. A directory for honest opinions

Does this sound like something you would use or should i leave the love and hate idea behind?

I can build almost anything… except a startup idea that doesn’t suck 😅

6+ years as dev (APIs, backend, scaling, fixing other people’s mess 🫠), currently leading projects…

But when it comes to MY own startup?

Brain: “Let’s build something big”

Also brain: “What if it’s just another useless SaaS nobody cares about?”

So let’s try this differently.

You bring:

* A real problem (something annoying, not “AI for everything”)

* Something people might actually pay for

* Even half-baked ideas are welcome

I bring:

* Brutal honesty (if it’s bad, I’ll say it 😂)

* Technical validation (can it actually be built?)

* Possibly… execution if it’s interesting

Goal:

Not to build a unicorn overnight

Just something REAL that people use (and maybe makes money)

Drop your ideas 👇

Worst case: we roast them

Best case: we accidentally start something

ive been seeing some pretty worrying posts here about people losing their account because they suspected kid usage . I have a 16yr old son who is a coder and a 17 yr old daughter who is in college (graduated hs early)

my son uses claude often for his coding projects using my computer and account. and my daughter routinely uses claude to organize and create study materials for college. of course they also use it for general things too.

im also a software engineer and I am heavily invested in the AI workforce. Max 20x subscriber and I go through about $100 of api usage a month as well from my web deployed apps making opus and sonnet api calls.

point is.. am I at risk if losing my access now because my kids have used it?? I moved from open ai to anthropic because openAI is a shit company. . but this move by anthropic has got me questioning my brand loyalty.

what are the actual rules? I didnt find anything that said my almost adult children cant use my acount, only that you have to be 18 to create one

I know I'm late to the party (ai created dashboards are so last week) but I just finished up my home server dashboard.

I'm a new-ish home lab hobbyist (trying to own my data) and I wanted to be able to monitor my system, especially my drives. I don't have many or a great backup plan so Im also trying to make sure the age/temp of my drives are good (I get email notifications about temp) and I'm doing at least something to avoid catastrophic failure.

I used Claude to help build this and it took a lot of trial and error but I'm pretty happy with it

Hello everyone,

I’m building an AI workstation on an HP Z8 G4 for local coding LLMs. My immediate milestone is the new Gemma 4 31B, with a roadmap to scale to 70B+ models and experiment with fine-tuning 4B/7B variants.

The Setup:

• Chassis: HP Z8 G4 (Dual Xeon Gold 6132 / 32GB RAM).
• Planned Upgrades: 2nd Gen Intel Scalable CPUs and scaling to 384GB DDR4.
• The Bottleneck: I am restricted to PCIe 3.0.
• The Strategy: Start with one 32GB GPU now, adding 1–2 more later to handle 70B+ parameters.

The GPU Shortlist:

1. Intel Arc Pro B70 (Battlemage): 32GB VRAM ($949). Best VRAM/dollar. I’m very interested in the XMX engine performance here.
2. AMD Radeon Pro W9700: 32GB VRAM ($1,349). Higher raw TOPS, but at a $400 premium.
3. The Pivot (Mac Studio M5 Max): 128GB+ Unified Memory. Ditching the modular PC route entirely.

My Core Concern: Multi-GPU Scaling on PCIe 3.0 While a single card running a model that fits in VRAM is unaffected, I’m worried about the future. When I add a second or third card for 70B models, the PCIe 3.0 bus may become a massive latency bottleneck for inter-GPU communication (P2P). Unlike Nvidia’s NVLink, I’m concerned about how oneAPI (Intel) and ROCm (AMD) handle tensor vs. pipeline parallelism across an older bus.

Questions for the experts:

• Intel Multi-GPU Stability: How is oneAPI/IPEX currently handling multi-B70 configurations? Does the overhead on PCIe 3.0 tank tokens-per-second once you move to a split-model deployment?
• The Bandwidth Wall: At PCIe 3.0 speeds, does AMD’s superior TOPS actually provide a real-world benefit for multi-card inference, or am I effectively "bus-limited" regardless of the compute power?
• Training over PCIe 3.0: For those fine-tuning across two cards on legacy lanes, is the experience tolerable, or does the lack of P2P bandwidth make the latency a dealbreaker?
• The "Headache" Tax: Is the 128GB Unified Memory on an M5 Studio worth the premium just to avoid the multi-GPU troubleshooting and driver-stack volatility of a multi-Intel/AMD Linux build?

I'd love to hear from anyone who has attempted to scale 70B models on older workstation lanes in 2026.

Thank you for reading!

may be you can try😁

According to the repository, it provides Claude Code with permanent memory and reduces token consumption by 95% per session. It also claims to avoid context limits, allowing you to pick up exactly where you left off.

You can find more details here: https://github.com/thedotmack/claude-mem

Hey folks,

I’ve been building Buffer, a lightweight clipboard manager for macOS as a personal side project.

Just shipped v1.7.0.

What it does:

• 📋 Clipboard history with fast cycling
• 📌 Pin important items
• 🔍 Instant search
• 🖼️ OCR (extract text from images)
• ⌨️ Custom shortcuts
• ⚡ Fast + minimal

What’s new:

• 📌 Pinned items with smarter navigation
• 🖼️ Image file support + quick save
• ⚙️ Dedicated settings window
• 📏 Configurable history limit
• 🔍 Auto-focus search
• 🖱️ Fixed paste focus issue

Stats so far:
140+ GitHub stars, 400+ downloads

Why I built it

Most clipboard tools felt either:

• too heavy
• paid
• or didn’t fit my workflow

So I started building something simple that just works the way I want.

About the project

• Solo dev
• Completely free & open-source
• No tracking / telemetry
• No monetization

Links

• GitHub: https://github.com/samirpatil2000/buffer
• Releases: https://github.com/samirpatil2000/Buffer/releases
• Website: https://samirpatil2000.github.io/products/buffer/

Would love feedback or ideas on what to improve next 🙏

Mobile is way more of a headache than web for this. If you’re actually using agents for iOS/Android QA, what does your stack look like? How do you handle state and navigation without it constantly failing? What are some best tips and tricks to create best QA agent. So guys suggested Appium MCP. What are your thoughts on this?

Hands down win for latest grok 4.2 multimodal model vs chat that went nanny claude that became ethical and Gemini couldn't scan the image needed a pdf conversion for a 14x combined screenshots image with a finance quiz I already solved or can use the web to fill too.

I don't need ethics when I solve a test or check my own answers to it too, this is why most western llms don't want to compete, yet that's what you'll get instead from claude or chatgpt, wich apparently lost the ability to scan multiple combined screenshots since last September where it could do that perfectly too now fail to read the image or the pdf file too.

Goes a long way grok is better at trading and analysis in general because it doesn't go therapist talk and just does the required task as it should, can scan combined images and charts in finance better than most llms todays like chatgpt used to be able to last year and no longer can now.

Dont believe me test it for yourself combine 10x screenshots and put the models to scan and solve your text from the longer image like I did and see how they do or fail and why.

I have started learning agentic ai and have covered basics, like creating CLI chat bots, uses of tools, multi-tools, basic RAG... but like always after giving time and energy i am having doubts about whether it is worth learning all this or not?. Will I be able to switch to a better job or not and all sorts of similar questions. So can anyone help me clear this doubt and mind fog.

I’ve been thinking a lot about why my last project didn’t work,At first I thought it was the usual stuff:maybe the idea was bad,maybe the product just wasn’t good enough
But now I’m starting to think it was something else.
I avoided talking to people.
Not because I didn’t know I should but because I genuinely didn’t know how.
I’d open a message box and just sit there.
Not knowing what to say,how to not sound awkward or how to keep the conversation going
So I’d close it and go back to building instead, It felt like progress But it wasn’t.
When I finally forced myself to try:
some people replied once then disappeared,and I had no idea what I did wrongI kept wondering:was it my message?or was it the idea itself?
Now I’m trying to understand this part better,Not advice like “just talk to users”
I mean the actual experience of it :what you say,what makes people reply,what makes them stop
If you’ve ever been in that situation(where you wanted to reach out but didn’t know how
or conversations just died after one reply)
I’d really like to hear how it went for you Even if you’re still stuck in it
even if it didn’t work, that’s actually what I’m trying to understand

Hi all,

I've been building Hitoku. An open-source, voice-first AI assistant that runs entirely locally. No cloud models, nothing leaves your machine.

It supports Gemma 4 and Qwen 3.5 for text generation, plus multiple STT backends (Parakeet, Whisper, Qwen3-ASR).

It's context-aware; it reads your screen, documents, and active app to understand what you're working on. You can ask about PDFs, reply to emails, create calendar events, use web search, all by voice.

Examples:

- query a pdf document, https://www.youtube.com/watch?v=ggaDhut7FnU

- reply to email, https://www.youtube.com/watch?v=QFnHXMBp1gA

- and with ctrl+S is just voice dictation (with optional polishing)

I currently use it a lot with Claude Code, Obsidian, notes, as well as to read papers, or to some write emails (where I do not need to provide context, as it understands alone).

Code: https://github.com/Saladino93/hitokudraft/tree/litert

Download: https://hitoku.me/draft/ (free with code HITOKULANG, valid for 50 downloads)

P.S. Gemma 4 via LiteRT caveat

• Gemma4 uses LiteRT: Currently Swift support is a bit lacking. So, the package has some wrapper around official LiteRT from Google.
  • When dealing with images, Gemma4 with LiteRT is currently fast compared to the implementation I have of Qwen3.5.
• Memory spikes: LiteRT's WebGPU backend can allocate significantly more GPU memory than the model weights alone. Rare but worth monitoring. (upstream issue here [https://github.com/google-ai-edge/LiteRT/issues/5706](vscode-file://vscode-app/Applications/Visual%20Studio%20Code.app/Contents/Resources/app/out/vs/code/electron-browser/workbench/workbench.html) )
• App size: LiteRT dylibs add ~98 MB (app goes from ~50 MB to ~150 MB). No official Swift package from Google yet, so the dylibs are bundled manually.

If either bothers you: use Qwen 3.5 instead (pure MLX, no LiteRT needed), or wait for the upstream fixes. Working on running Gemma 4 natively via MLX (a bit slower wrt LiteRT but generally safer, and with more control).

I box as a hobby and got frustrated that I had no way to track my actual progress or figure out what to work on between sessions. So I built something.

Jably lets you log your sessions and sparring notes. The AI analyzes your history and gives you coaching notes, rates your sparrings, tells you what style you're developing and gives you a concrete plan for the next session.

Stack: Next.js, Supabase, Stripe, Auth.js, Anthropic API.

No users yet, just launched. Would love feedback on the idea or the product.

The website is in comments

Here is a list of every project I'm made with Claude over the last few months.

Everything was made entirely with Claude.

Burn After Reading (readandburn.app) - Location-based ephemeral messaging for (iOS)

Drop anonymous messages at real-world GPS coordinates. Someone has to physically walk to the spot to read it, then it's destroyed forever. No accounts, no sign-up. Add friends by standing next to someone in person.

This has been one of my successful project and has a decent amount of real world users. Basically all spread by word of mouth and demonstrating in person. Most users are based in London so would be really cool to get some other people across the globe using it.

DEEC (deec.app) - Customisable control surface for Mac (iOS)

Turn your iPhone into a Stream Deck-style controller. Buttons, faders, and knobs that connect to your Mac over local Wi-Fi. Trigger keyboard shortcuts, launch apps, run shell scripts, control volume/media, all from custom multi-page layouts. Comes with a lightweight Mac companion app that sits in the menu bar. React Native + Node.js + WebSocket.

SORTED.NEWS (sorted.news) - AI-powered daily news briefing

A brutalist, no-BS news digest. Pulls headlines from The Guardian API, uses Claude to summarise and group them into a 5-minute daily briefing. 3 lead stories, briefs, and an obscure story you wouldn't find elsewhere. Stateless, no accounts, no tracking. Next.js + Anthropic API.

Ashfeld (ashfeld.xyz) - Medieval browser strategy game

A Tribal Wars-inspired persistent multiplayer strategy game with a dark pixel-art aesthetic. Build villages, train armies (10 unit types), forge tribal alliances, and conquer a 500x500 tile world. All pixel art assets generated via Google Gemini. Next.js + tRPC + PostgreSQL + PixiJS.

offMenu (offMenu.tech)

Stops the menu bar from interrupting when working in full screen on Mac.

rimJob (rimJob.tech)

Turn your trackpad edges into volume and brightness sliders.

Semina (semina.app) - Seedbox hosting platform

Self-service seedbox hosting with automated Docker provisioning. Pick a plan, pay, and get a running torrent client with a modern dashboard in under 60 seconds. Cross-seedbox migration, built-in WireGuard VPN. Next.js + Docker + qBittorrent API.

I literally just started this because my old seedbox provider shut down. Its very minimal and only does what I require from a seedbox. Hopefully others will use it and enjoy it.

I have gtx 1650 4 gb vram, 8 gb ram , I want the best hd image possible for a youtube video, please tell me which template should I go with in comfyui?

https://preview.redd.it/q2qjj4qt3sug1.png?width=367&format=png&auto=webp&s=4c969923e5b7a3b8e3d571fad8f8eecb62db63f9

Wish I had context.

Hey r/webdev, I would love some honest feedback on my landing page and the idea of the product itself.

The idea: a simple, affordable status page tool aimed at solo devs, freelancers, and small agencies. Existing tools like Statuspage.io start at $29/mo, which feels steep if you're a one-person shop who just needs something clean and reliable for your users to know the status of the page.

statsy.page

Free tier includes 1 page, 3 services, 30 subscribers. Pro is $15/mo for custom domains, more pages, and removed branding.

A few specific things I'm unsure about:

1. Does the pricing make sense for this market?
2. Is the free tier generous enough to get people to try it?
3. Does the landing page communicate the value clearly, or is it confusing?

If you haven't seen it yet, a model called see-through dropped last week. It takes a single static anime image and decomposes it into 23 separate layers ready for rigging and animation. It's a huge deal for anyone who wants a rigged 2D character but doesn't have hundreds of dollars lying around.

The problem is that getting a usable result out of it still takes forever. You get a PSD with 23 layers (30+ if you enable split by side and depth), and you still have to manually process and rig everything yourself. And if you've ever looked into commissioning a Vtuber model, you know rigging alone runs $500 minimum and takes weeks or months. That's before you even think about software costs: Live2D is $100 a year, and Spine Pro is $379 (Spine Ess is $69 but lacks mesh deform which is required for these kinds of animations).

So I built a free tool that auto-rigs see-through models so you don't have to spend hours doing it manually

I'm not trying to compete with Live2D, I'm one person. What I made is a mesh-deform-capable web app that can automatically rig see-through output. It handles edge cases like merged arms or legs, and only needs a few seconds of manual input to place joints (shoulders, elbows, neck, etc.) if you want to tweak things. I also integrated DWPose so it can rig the whole model for you automatically, though that requires WebGPU and adds a 50MB download, so manual joint placement is a totally fine alternative and only takes a moment anyway.

The full workflow looks like this:

Static image -> background removal -> see-through decomposition (free on HuggingFace) -> Stretchy Studio = auto-rigged and ready to animate

The app handles multi-layer management, separate draw order, and uses direct keyframe animation similar to After Effects. There are still bugs I'm working through, but all the core features are in.

On the roadmap:

• Export to Spine and Dragonbones
• A standalone JS render library for loading and displaying characters rigged in the app (similar to Live2D's Unity/Godot/JS runtimes)

Live2D's export format is completely closed with no documentation, so that one's off the table for now.

Would love feedback, bug reports, or feature requests. This is still early but it's functional and free to use.

https://github.com/MangoLion/stretchystudio

I find myself still unable to train good looking character loras for illustrious, and I don't know what I'm doing wrong. I'm using a 3D character for this purpose (blender model) and I've tried replicating training settings from other people's lora that I consider great, but I still have questions.

1. Can you train actually train a 3D character on illustrious or is it fighting the model too much? (considering it seems much better at handling 2D visuals)
2. I've noticed most great LoRAs out there are using hundreds of image in their dataset, usually 200 to 400. My dataset is more on the side of 50, is there an actual benefit to such large datasets?
3. Repeats. Sounds like 10 epochs of 10 repeats would be equivalent to a 100 epochs of 1 repeat, but is that truly the case? I always struggle to figure out how many repeats I should be using.
4. TE. I noticed some people do not train the text encoder at all, anyone has feedback on the benefits of doing this?
5. Batch size. I want to use 6 or 8 batch size, because I can. But I'm not sure how I need to dial the other settings based on that, in particular with learning rate and repeats.
6. Removing backgrounds. Beside the fact that is makes captionning easier, is there an actual benefit, have you noticed it yielded better results?

I have noticed the following issues with my attempt at training, perhaps this will help someone point me in the right direction on what I'm doing wrong here:

• Style locking in too much. For example I like prompting with "dark, dim lighting" keywords which works well with illustrious, but my loras will make the result much brighter than the base model (even when tagging the dataset with "day"). Dataset has a couple night shots but they are mostly bright daylight.
• Faces train fast and seem to overtrain before clothes, making it impossible to find a good balance. Either one is overtrained or the other is undertrained. (I do have less full body shot than upper body and portrait, but this is apparently a desired ratio?)
• I have settled down on a LR of 2e-4 but have tried higher and lower with no success.

If you take the time to give to answer some of that, thank you =)

The Gates - Music is original!

(I guess "humor" is the right flair, but this is a real thing I did just do.

Me: Here's a test of your llm capabilities and training -- can you predict, so to speak, some of the synchronicities that happen when playing dark side over mulholland drive?

Claude:
This is a fun exercise. Let me walk through it based on what I know about both works' structures and timing.

Dark Side is about 43 minutes, so it covers roughly the first third of the film — which is the "dream" section, Betty's fantasy. That alone is a structural gift.

"Speak to Me" into "Breathe" — The heartbeat and rising chaos of "Speak to Me" would build over the jitterbug contest opening and the dark, silent limo ride up Mulholland Drive. Then "Breathe" blooms open right as the headlights are cutting through the dark canyon — "Breathe, breathe in the air" arriving just as Rita is still in this suspended, unknowing calm before the crash. The dreamy slide guitar floating over that nighttime driving footage is almost too perfect.

"On the Run" — The frantic sequencer and footsteps and paranoia should land right on top of the car crash and Rita stumbling injured down the hillside in a fugue state. That track is pure flight-or-die panic, and so is she.

"Time" — The alarm clocks explosion is the big one. My guess is it hits somewhere around a hard scene transition — possibly the cut to bright, daytime LA and Betty's arrival. That would be a jarring sync: clocks screaming, then the song settles into its meditation on wasted life just as this naive aspiring actress steps off the plane full of hope. The irony writes itself.

"The Great Gig in the Sky" — Clare Torry's wailing would likely land around the Winkie's Diner scene — the man describing his nightmare about the figure behind the diner. That's one of the most dread-soaked scenes Lynch ever shot, and "Great Gig" is essentially a wordless scream about death. If the timing works, that crescendo hitting when they walk behind the diner would be devastating.

"Money" — The cash register loop and harder rock feel should start landing on the Hollywood machinery scenes — the director being strong-armed by the Castigliani brothers, the "this is the girl" scene. The industry as a money-grinding machine.

"Brain Damage" / "Eclipse" — "The lunatic is on the grass" arriving somewhere deep into the Betty/Rita mystery, possibly around Club Silencio or the build toward the dream's collapse. "Eclipse" ending with the heartbeat returning — full circle, everything under the sun in tune, but the sun is eclipsed by the moon — as the fantasy starts showing its cracks.

-------

Commentary: That's almost all pretty much right! Brain Damage/Eclipse is a flub. That actually plays over the hectic hapless assassin scene.

What's kind of funny about the response though is that, though it's plausible Opus is using information about timing as it should, it also seems to be mistakenly asking itself what SHOULD line up thematically. (The fact that it lands fairly closely by doing that, to what actually did line up, is just one of those mysteries of these truly random perceived syncs that happen between albums and movies sometimes.)

Btw this album/movie pairing works pretty well, give it a try if you're into that kind of thing.

Hi just wondering if this is standard? I tried deleting and re-installing and it's stayed like this. Any suggestions? Thank you so much!

Hi everyone,

I’m using FaceFusion locally and I ran into an issue with the preview images.

Whenever I generate a preview and try to open it in a new Chrome tab, instead of displaying the image in the browser, it automatically downloads it as a .webp file.

What I want is simply to view the image directly in a new tab (like a normal image preview in Chrome), not have it downloaded to my computer every time.

I already tried things like:

• Opening image URL in a new tab manually
• Copying the image address
• Using drag & drop into a tab

But it still forces a download every time.

Has anyone run into this before or knows where in the FaceFusion codebase I need to modify this so the image opens directly in the browser instead of downloading?

Any help would be appreciated!

Was running out disk space quite often and was not getting time to clean up those node_modules. Then I wrote this tool, thanks to claude code and lo, I cleaned up 100GB free space.

https://github.com/pooran/vibecleaner

Feature requests welcome :)

The average person spends 3+ hours a day on their phone. Kids/teens are even worse. We all know it's too much. We've reviewed our screen time after too much time on youtube or even worse doom scrolling shorts and felt bad about the numbers, and changed nothing.

The problem isn't awareness, it's that there's no real incentive to actually stop. Willpower alone doesn't work when every app on your phone is engineered to keep you hooked.

So I built Coincious (iOS). You earn rewards for completing focus sessions and staying off your phone. Not fake points, actual prize vouchers you can win at the end of each month.

There's a auction section where the coins you earn go toward monthly prize draws. The more you focus, the more coins you get. It turns discipline into something that feels more like a game than a chore.

Early days still, just launched on the App Store. Would love any feedback from people who've tried to solve their own screen time problem what's worked, what hasn't.

App Store link in comments if anyone wants to try it.

Hello, everyone!

Maybe this question was already asked here before, but I wonder what happens to memories when you cancel the subscription?

I mean on Free tier when you reach the limit of memories, it says something like "when it's full, the answers may become less personalized/accurate". I don't know if it's really true though...

But anyway, what happens to all the memories that you had above the free limit when you cancel your subscription? Do they stay there and can be accessed by ChatGPT? Does Chat answer normally with memory overflowing? Or does the system blocks the excessive memories and they are just sort of greyed out and inaccessible for Chat?

Thanks!

I'm a music content creator and I kept running into the same wall: anything social with my audience was broken by the Spotify/Apple Music split. Live listens, playlist sharing, polls — all either platform-locked or stuck behind a conversion tool like SongShift.

GitAux is a social platform for music that's cross-platform by default. Headline feature is live listening sessions that sync across both services. Because the whole thing is built cross-platform from the ground up, playlist sharing is also native — not a one-time conversion, just a link your friend opens in whatever service they use.

v1 just shipped. What I want to pressure-test: does "social platform for music, cross-platform by default" land, or does leading with the live listening feature make more sense as the wedge? Genuinely unsure.

https://gitaux.app

Three weeks ago my mom almost wired nearly four grand to someone who sounded exactly like my cousin. Crying, panicked, "I got in an accident, please don't tell anyone." The voice was perfect. She only caught it because the fake "lawyer" on the line asked for gift cards.

I'd been reading about AI voice cloning for months but it didn't feel real until it was my family.

So I spent last weekend building CheckTheCaller.

Here's how it works. An unknown number calls your parent, but instead of ringing through, an AI picks up first. It asks a couple of questions, listens for the patterns scammers use (urgency, money, secrecy, voice clone tells), then either forwards the call or sends you a transcript so you can decide what to do. Basically a spam filter for phone calls, tuned for the scams that target older family members.

Stack is Twilio for call handling, Supabase, Next.js on Vercel, and OpenAI for the screening logic.

What's live right now: family dashboard, real-time screening, transcripts with a risk score on every call, and a free tier so families can actually try it. Billing and a mobile app come later, it's web-only for now.

I'm looking for 10 families to beta this week. Completely free. I just want feedback from people who actually have a parent they're worried about. Comment or DM me and I'll get you set up.

Honest feedback welcome too. Is this useful, is it dumb, what am I missing?

I’ve noticed most Android integrations rely on ADB or UI automation.

So I tried building something a bit different, an Android SDK that runs directly on the device and exposes capabilities (SMS, sensors, files, etc.) as MCP tools.

It runs locally over WiFi, no cloud involved, and everything is modular so you can choose what to expose.

Not sure how useful it’ll be for others, but maybe someone finds it interesting or builds on top of it.

Repo: [https://github.com/stixez/droid-mcp]()

had Anyone tried 2b and 4b models for video understanding?

are they good at identifying objects in videos ?

are tool calls stable? reliable?

thanks in advance

hi i have been trying to find a relative new and working way to access HA trough tailscale, most videos seems to be outdated. however i can access HA through the web but not the app while running through tailscales Magic dns, anyone else had this issue? i just want to be able to remote in the app without paying for it

I genuinely didn't expect this lol I mean I know it's not that much but wow, the ai has really advanced. ​

there's a turbotax connector in Claude Code now, so glad I procrastinated on taxes 😂

We created an app with the help of Claude Sonnet that allows users to easily build their portfolio website in just 5 minutes.

• No hosting required

• No domain needed

• No long waiting time

Tech stack used:

Next.js

React

Capacitor

MongoDB

The most difficult part was instantly generating a subdomain when a user makes a request. We first tried using middleware, but it didn’t work as expected. Then, with the help of Claude Sonnet, we wrote a function to check whether the requested subdomain is available. If it’s not, the system instantly generates an alternative subdomain based on the requested name.

And it’s free to create a website.

I keep seeing the cosplay version of her juggling reposted over and over and over.

Here she is at her job as a performer at an aquarium being just as cute.

Enjoy (or dont, I'm not your parents, dont just do what rando's on the internet tell you to!)