AI-Ranked Reddit Feed

Source: https://www.genesis.ai/blog/gene-26-5-advancing-robotic-manipulation-to-human-level

I have tried many AI writing tools, but Claude is the first tool to really change the way I edit long texts.Not because its answer is the most dazzling, but because it is better at following the whole content. If I post a messy draft, a long email, a rough article, or even a note where I want to write, it usually understands the whole structure, not just modifying a few isolated sentences.For me, the biggest difference is tone adjustment. I can make it less defensive, more concise, more professional, or less "AI flavor", and it usually retains its original meaning. This is very important when modifying personal content or work content, because I don't want a text to be changed to look like it was written by another person.I will also use it to summarize the content, but not the most basic summary. It's more like asking it: "What does this paragraph really mean?" "Where is this argument weak?" "which part is repetitive?" This usage is very helpful for long documents.I don't think Claude replaced my writing. It changed my editing stage more than my writing stage. I still need to know what I want to express, but Claude can help me re-read the draft from an external perspective.

I am new in ai and making an ai powered app basically an image genaration or filtering it without any image like so... Chatgpt told me to use open ai api paid keys.

Can't we make without that the ai agents and all? It's necessary for this?

Can anyone help me with this knowledge Please 🙏

Is there a place where I can read real use cases / actual deployments of AI Agents in real scenarios?

The internet is flooded with examples similar to below but these in my head are not true AI Agents right?

1. If email arrives with pdf, check pdf for invoice information and put it in a google sheet is not a AI Agent? Its a workflow that now has llm call as a node

2. Check my google search console and suggest ideas for SEO - This again is a cron job (run every xhrs), collate information and feed it into a llm to generate ideas. This is a workflow as well.

3. personal assistants - I ask for information and llm figures out which tool to call and gets it and writes to a database

perhaps coding agents which do some stuff autonoumously when prompted is a good example. Is there a compilation of real use case anywhere online?

Not "would you use it less" or "would you pay more." Just: would you phrase requests differently? Respond to mistakes differently? Does the certainty of their experience change your behavior, or does the $20/month override that?

No wrong answer. Genuinely curious what the threshold is.

I made a Claude Code plugin so two terminals can phone each other — /qu asks, /ans answers

If you run multiple Claude Code sessions, you've probably hit this: you solved something a month ago in an old session, and now you're doing it again in a new one. You could dig through the old session manually. Or you could just get the two sessions to talk.

That's what this does.

How it works:

1/ New terminal hits a problem. You open the old session that solved it.

2/ Type /ans in the old terminal. It picks up and waits.

3/ Type /qu in the new terminal. It dials.

4/ Leave them alone. The /qu session reads its own context, figures out what to ask, exchanges messages with /ans, and reports back with a summary.

Either order works — whichever is second waits up to 30s for the first. Zero RAM at rest, nothing to clean up, state lives in /tmp and disappears on reboot.

Repo (readme has a full worked example if the concept sounds abstract): https://github.com/startupfundraising/claude-code-chat-bridge

Happy to hear if anyone tries it.

Microsoft has released their long abated answer to Claude in Excel and it is Claude. Has any tested this crazy mash up?

We monitor latency, cost, errors all the usual stuff.

But quality feels invisible.

Sometimes the system just slowly starts giving worse answers and nobody notices until users complain.

Do you have any way to track this proactively, or is it still mostly reactive?

So. I've been thinking of this for a while and began building it half a year ago. I've finally managed a working prototype and figured I'd ask people here what they think of the idea.

Basically, it's Reddit mixed with Facebook and Twitter. Tags become subredits, without moderation, without any algorithm, just chronological. If you write a post without a tag, it just becomes your personal post on your own page. Your feed is posts from all your friends or tags, nothing else. You have a gallery, chat, etc, just like FB.

The biggest difference is that all your content, your posts, images, videos, etc.. are stored locally on your device. As such, that stuff is only available to others while you are online, unless they have it cached locally. You can keep the app running in the background so your content is available, or exit it and become only a profile name to new people.

There's no encryption or anything for your profile. Your profile is just your name and your password. If you lose either, you just make a new one, all your content is still on your device, anyway. You can clone the content from one device to another to make it easier to go from a pc to a phone, for instance.. or always keep the app on in the background on your phone so your content is accessible.

The webpage and app are just portals. Just handshakes, that connect you to others. Both are open and anyone can make their own version of the app or the webpage, the usernames are stored with each user, when a new one is made, it is made unavailable to everyone else that connects to the network, with the "earliest" being the winner in a conflict.

It's free, open, local first and completely decentralized. In theory, every device is a server and every client is infrastructure.

Thoughts?

So I signed up for the 30 day trial. The trial was available for either business or standard/plus plan. As far as I could see the standard/plus plan includes everything in the business plan but more, so it made sense to go with that one.
Plus, when I tried to select the business plan it asked for a business email but when I selected the standard/plus plan, it allowed me to sign up using my regular email address. So I didn't need a business email but got everything in the business plan + standard/plus plan.

The issue I am having is, it asked me to add a payment method, which I did, but I can't find anywhere where it says I am on a free trial apart from when I click on the app it had me create. Once I click on that, there's a small banner that says I am on a trial. It's not under subscriptions or anywhere else. So I don't know how I am meant to cancel it before the end of the trial if I do not want to use it.

Also, how do I use Veo 3 with this? I went to Agent platform / studio / generate media / video. I think this is the Vertex AI or something? I've never used this before, so it's a little confusing. But under the model settings, it says task > text-to-video and then it says model > veo 3.1 but it says charged will apply for video + audio generation. $0.40/second. This leads me to believe, if I generate media, it will charge my payment method instead of using the trial?

Have I done this incorrectly or something?
How do I check my trial is being used and I'm not outside of my trial using something that will charge me?

Thanks

So I signed up for the 30 day trial. The trial was available for either business or standard/plus plan. As far as I could see the standard/plus plan includes everything in the business plan but more, so it made sense to go with that one.
Plus, when I tried to select the business plan it asked for a business email but when I selected the standard/plus plan, it allowed me to sign up using my regular email address. So I didn't need a business email but got everything in the business plan + standard/plus plan.

The issue I am having is, it asked me to add a payment method, which I did, but I can't find anywhere where it says I am on a free trial apart from when I click on the app it had me create. Once I click on that, there's a small banner that says I am on a trial. It's not under subscriptions or anywhere else. So I don't know how I am meant to cancel it before the end of the trial if I do not want to use it.

Also, how do I use Veo 3 with this? I went to Agent platform / studio / generate media / video. I think this is the Vertex AI or something? I've never used this before, so it's a little confusing. But under the model settings, it says task > text-to-video and then it says model > veo 3.1 but it says charged will apply for video + audio generation. $0.40/second. This leads me to believe, if I generate media, it will charge my payment method instead of using the trial?

Have I done this incorrectly or something?
How do I check my trial is being used and I'm not outside of my trial using something that will charge me?

Thanks

Title:
Is anyone actively using Flux2.DEV with good results?

Body:
Hi everyone,

I’ve been trying to use Flux2.DEV actively for the past few months, testing it from time to time, but I still haven’t been able to get results that I’m happy with.

The biggest issue for me is that I can’t seem to get sharp, realistic-looking images from it in the same way I can with models like Z-Image Turbo. Even when I increase the resolution or raise the step count, the final images still tend to look somewhat hazy, soft, or foggy.

I’ve also tried changing samplers and experimenting with different settings, but the results still don’t feel very satisfying to me.

At this point, I’m wondering if the issue is related to the training data, the Flux2 VAE, the scheduler, or if I’m simply missing the right workflow/settings.

The image editing feature also hasn’t felt strong enough for me to justify using Flux2.DEV heavily, and the LoRA ecosystem seems almost nonexistent so far.

I really wanted to make good use of this model, but most of my final outputs still end up looking too soft or unclear compared to what I can get from other models.

For those of you who are getting good results with Flux2.DEV:

• What are you mainly using it for?
• Are there specific settings, samplers, schedulers, or workflows that work well for you?
• Do you think Flux2.DEV has a particular strength compared to other image models?

I’d appreciate any practical examples or advice.

I just subscribed ollama cloud and noticed the documentation actually suggests the integration to many coding agents.

Which one you guys actively using and which model to it ?

Stating his birth year after saying he wasn't alive in 1939-45🤦‍♀️😭

spent the last 6 weeks evaluating sandbox approaches for running AI agents 24/7 and the tradeoffs are way more nuanced than the docs suggest.

docker is the obvious starting point but the shared kernel breaks down once an agent has sudo or pulls untrusted code. 'restart the container if it goes sideways' stops being good enough at scale, the blast radius is the whole host.

firecracker boots in around 125ms with a real kernel boundary which is what aws lambda runs underneath. management surface is heavier than docker compose but the isolation is the part u actually want for long-running agent workloads.

gvisor intercepts syscalls without needing a separate vm. the boot overhead is reasonable but io-heavy workloads take a real throughput hit. ran into this on a logs-shuffling agent and lost about 30% relative to plain docker, ended up moving that one back to docker bc the security profile didnt justify the cost.

kata containers gives strong isolation under k8s but the 1-3 second cold start kills any reactive workload. fine for batch jobs that wake up and process a queue, painful for anything user-facing.

cloud-hypervisor is the underrated one in this list, similar boot to firecracker, cleaner config story, smaller community though so the documentation is thinner and stack overflow is mostly empty.

ended up with firecracker for the production agent workloads where the agent needs sudo or runs arbitrary code, and kept docker for ephemeral one-shot agents that touch nothing sensitive. the 'firecracker for sensitive workloads, docker for everything else' split has held up for 5 weeks.

one thing the docs skip: getting nbd-client + a real init system inside firecracker that doesnt eat 60mb of ram. that took longer than picking the runtime.

also whart is the best course for automation

Looking for real-world advice from people actually using local LLMs daily for knowledge work / executive thinking workflows.

Use case is personal (single-user only), not hosting or serving others.

What I want to build:

- Personal AI advisor / assistant

- Obsidian-integrated RAG

- Book + PDF repository (in external disk)

- Long-term memory / contextual assistant

- Agentic AI experimentation (hands-on learning)

- Strategic thinking, management consulting-style analysis, writing, synthesis

- Privacy-first local setup

- Picking up coding again (to build application as hobby)

Current shortlist:

- Mac Studio M3 Ultra 28-core CPU / 60-core GPU / 96GB RAM

vs

- Mac Studio M3 Ultra 32-core CPU / 80-core GPU / 96GB RAM

Planned models:

- Qwen 70B mainly

- likely Q5_K_M quant (maybe Q4_K_M initially)

A few questions for people actually running similar setups:

1. Is 96GB realistically enough for Q5 70B + RAG + agent workflows for the next few years, assuming mostly one active model at a time?

2. Does the jump from 60-core GPU to 80-core GPU materially change the experience in real life, or mostly benchmark numbers?

3. For nuanced writing / emotionally aware outputs / consulting-style reasoning:

- how noticeable is the jump from Q4_K_M to Q5_K_M?

- does Q5 feel meaningfully more “human” or coherent over long sessions?

1. If you also use paid ChatGPT / Claude:

- where does local Qwen 70B Q5 still noticeably fall short?

- where does local actually feel better once RAG/personal memory is integrated?

1. Any regrets going Mac Studio instead of NVIDIA/CUDA workstation for this type of workflow?

Not looking for benchmark flex or homelab setups — more interested in lived experience from people using local AI as a daily thinking companion / knowledge system.

Thank you again.

I was asked for this guide, so here it is. Some overlap with someone else’s post from yesterday. YMMV! Too busy with work to write myself, so I asked Opus to write for me (I have validated the content!).

I’m sure there will be debate over using q4 blah blah. I’m happy with how it works with my models. I am happy to create higher q models as far as my hardware allows, if asked!

NextN MTP gives ~2.9× decode on the Qwen3.5/3.6 family vs vanilla, zero quality loss (head ships with the model). Heavy MoE arch like 35B-A3B hits ~150 tok/s on a 3090 Ti. Catch: it's not merged upstream as of this writing — you need to pull the open PRs.

Required PRs (cherry-pick or build off the branch they live on)

Both open as of May 2026 — track + rebuild when they ship:

1. #22400 — llama: allow partial seq_rm for GDN models for speculative decoding https://github.com/ggml-org/llama.cpp/pull/22400 Prerequisite. Adds keep_intermediates path for GDN/SSM models so spec-decode can rollback partial draft. Without this, MTP doesn't function on hybrid-attn models (27B).

2. #22673 — llama + spec: MTP Support https://github.com/ggml-org/llama.cpp/pull/22673 The main course. Adds qwen35_mtp + qwen35moe_mtp arch loaders, NextN graph forward, --spec-type mtp flag, and the speculative state machine.

Either rebase both onto current upstream master, or pull am17an's branches directly.

My fork (FYI — has both PRs merged + extras)

https://github.com/nickstx/llama.cpp branch crucible

Has #22400 + #22673 plus a qwen3moe_mtp arch (Qwen3-Coder base — work-in-progress for coder-30B MTP head training, not needed for Qwen3.5/3.6 release models). For ready-to-build, this is the simplest pull. Also includes some unmerged slot PRs, that added support for cross-PID slot resumes.

Build (CUDA)

bash git clone https://github.com/nickstx/llama.cpp.git cd llama.cpp git checkout crucible cmake -B build -DGGML_CUDA=on -DCMAKE_BUILD_TYPE=Release cmake --build build -j$(nproc) --target llama-server

Get a working GGUF

You want the Q8nextn variants — these have the NextN block override applied (most public quants either strip nextn or quantize it to Q4 →s less ancceptance).

Collection: https://hf.co/collections/localweights/qwen36-mtp-crucible-release-69fbdeadca3472e779dff9d2

Or roll your own from a bf16 source:

```bash

Optional: imatrix calibration (5-8% PPL gain)

./build/bin/llama-imatrix -m model-bf16.gguf -f calibration.txt -ngl 999 \ --chunks 200 -o imatrix.dat

Quantize WITH nextn override (this is the part everyone misses)

./build/bin/llama-quantize \ --imatrix imatrix.dat \ --tensor-type nextn=q8_0 \ model-bf16.gguf model-IMAT-IQ4_XS-Q8nextn.gguf IQ4_XS ```

--tensor-type nextn=q8_0 overrides quant for any tensor matching nextn. Without it: //////// output.

Run

bash ./build/bin/llama-server \ -m Qwen3.6-35B-A3B-MTP-IMAT-Q4_K_M-Q8nextn.gguf \ --port 8080 -ngl 999 -fa on --parallel 1 \ --ctx-size 131072 -ctk q8_0 -ctv q8_0 \ --kv-unified \ --spec-type mtp --spec-draft-n-max 4 \ --metrics --jinja

Key flags: - --spec-type mtp — enables NextN draft path (this is the new flag from #22673) - --spec-draft-n-max 4 — propose 4 tokens/step. Bump to 6 for chat (longer ctx, predictable). Drop to 2 for code. Default 4 fine. - -ctk q8_0 -ctv q8_0 — KV at q8 saves ~half VRAM, no quality cost on this family. - --kv-unified — required for spec-decode.

Speed (3090 Ti, 350W/1700MHz, q8 KV, ~50-tok prompt → 1600-tok decode)

35B-A3B beats 27B 3× (A3B = 3B active params, MoE wins). 27B is dense+SSM hybrid → slow link.

Power tuning (3090 Ti)

For sustained MTP workloads, 350W + 1700MHz lock is the tok/W sweet spot: bash sudo nvidia-smi -pl 350 sudo nvidia-smi -lgc 0,1700

300W default makes the clock collapse to ~1080MHz under MTP draft passes — costs ~17% tps. Don't drop below 280W. Persist via systemd if you want it across reboots.

Gotchas

1. **//// output** = nextn block was Q4-quantized. Re-quant with --tensor-type nextn=q8_0.
2. No speedup = --spec-type mtp not on, or model has no nextn tensors. Verify: llama-gguf model.gguf r | grep nextn.
3. OOM long ctx = drop KV f16→q8, shrink ctx, partial offload.
4. 27B bf16 dump has inf at blk.18.ffn_up. IQ4 kernels handle it; Q4_K_M validation aborts. Use IQ4_XS for 27B if Q4 fails.
5. Spec draft accept rate: check /metrics endpoint — spec_decode_*_total. Code: ~50-65%. Chat: 70%+.

Credits

• am17an / Aman Gupta for both upstream PRs
• Qwen team for shipping NextN-trained heads
• ggml-org for the runtime

My setup for my local LLM right now is Ollama for claude using qwen 3.5

my problem is it cannot do changes on my files

it can read my directory for analyze bug fixes etc. but it cannot do changes on the files.

is there a way for my local LLM to have full access on my system? and do some changes on my files, do some terminal work like git.

I was a little confused, I thought I gave my LLM acess to my system/file, but I guess its on read-mode only???

ChatGPT told me to use Aider or Open interpreyer.

I want to know if this is my only options, maybe there are just settings I need to tweak here and there.

I've also read about using Opencode on this subreddit, I'm a little interested in that option,

I also wanted to ask actual people for this kinds of questions, If some of you guys have similar setup to me, maybe you can help me out, massively appreciated.

P.S my machine is not powerfull but I can wait for AI to do its work.

processor: AMD Ryzen 7 5700U with Radeon Graphics (1.80 GHz)

ram: 16.0 GB

graphics: AMD Radeon(TM) Graphics (496 MB)

I have tried to animate silhouettes—outlines of people filled with black, no details. I get "no pictures of prominent people" error. I tried to upload https://daily.jstor.org/wp-content/uploads/2022/01/graffiti_jaytalking_in_19th_century_paris_1050x700.jpg which is a colorized photograph of the Moving Sidewalk and elevated train at the Paris Universal Expo, ~1899. I get "no pictures of minors" error. I wake up in the morning after a good night's sleep and first thing try to generate something. I get "unusual activity" error. I upload a square image but need landscape and do get it by saying "extend image, keep details". I think oh maybe really change this or that detail, I get "against our policies" error. I generate images from a prompt, choose the most fitting one and try to animate or modify it. I get "no prominent people" error.

I guess my question boils down to: when using the app after an 8 hrs break constitutes unusual activity, when shadow people and images just generated by the app itself are deemed too famous to be touched, when a 125 yrs-old, slightly messy, hand-colorized photo of a busy street scene with people who are dressed down to their ankles, up to the chin and hat on top triggers CSAM, then, please, could you tell me what the intended use of the app is? I'm afraid I'm having trouble figuring it out myself.

I built a tool called swokei that analyzes business websites for issues in design, structure and mobile optimization. It then generates personalized outreach messages that are ready to send directly inside the platform. You can also run full email automation campaigns within the platform.

Hi

I am starting to build parts for my new pc and wanted to know what new possibilities (or improvements) this new build

For now I only do image gen (mostly illustrious) and LLMs (LM studio) but want to slowly try other things like video/sound/music/agents.

What are the best things for each branch I could run with a setup like this ?

Actual build

* CPU : Ryzen 5600 X

* GPU : 3060 12gb

* RAM : DDR4 32gb 3200MHz

Projected build

* CPU : Ryzen 7 7800X3D (already bought)

* GPU : 5070 Ti 16gb

* RAM : DDR5 32~64gb 5600~6000 MHz

Thanks.

I cannot even process what happened today. We built this whole system around an anti bot browser agent using stealth web scraping techniques for MFA browser automation. Thought we were so smart using a fancy AI agent browser tool that relies on fixed CSS selectors to interact with client websites. Our demos even featured our human like web automation.

This morning the main client site does a tiny UI refresh. They change one button class from 'submit btn primary' to 'btn primary submit'. Thats it. Our entire automation pipeline explodes. Every single task fails because the selectors no longer match. Hundreds of pending jobs across 15 client accounts just halt. Production scraping stops dead. Users see errors everywhere. Support lines blow up.

I spent the whole day in emergency mode manually clicking through browsers while our team scrambles to update selectors. Turns out this has happened four times in the last year with different sites. We are stuck in this constant maintenance hell because the tool depends on these fragile fixed structures. Clients are yelling about SLAs and we look like complete idiots.

Need advice on changing to something like computer vision AI for browser tasks that adapts without breaking every time. Has anyone else had their browser automation tool nuke production from a minor UI tweak?

After my posts on multi-LLM coding landed well last week, I went full rabbit hole mode and built a proper polished version.

Basically you can fire up multiple code reviews either using tmux or headless sessions of the CLIs you already pay for Claude Code, Codex, Gemini, OpenCode, etc.

I found that relying on one LLM isn't good enough. Even Opus 4.7 at max effort makes plenty of mistakes. Throwing other LLMs in the mix made a huge difference. Last week I had Opus approve a PR clean, Kimi flagged a missing tenant check on a service-role query, and Gemini caught a race condition in a retry loop. Three reviewers, three different bugs, one PR.

Initially I ran Opus with Codex, then added Gemini, and now Chinese models like Kimi and Deepseek. Started off doing it manually, then got Claude to coordinate it via tmux sessions, which works but is clunky to manage. Now there's a headless mode too, and you can kick off reviews straight from MCP commands inside whatever CLI you already use.

I also added a fallback option, so if one LLM runs out of quota it retries with another. You can pick unanimous or majority consensus. You can also assign a persona to each LLM , one looks at security issues, another at architecture drift, etc. It piggybacks on the CLI subscriptions you already pay for, so no extra API bills stacking up.

Added a nice UI to the whole thing so it's easy to manage and visualise. Fully open source. No paywalls, no freemium b.s.

Repo link in the comments if anyone wants to give it a go.

I tried running AesSedai/MiMo-2.5-GGUF:Q4-K-M under llama.cpp (main tree, compiled 36hours ago)

Hardware: nvidia A6000 with 48GB RAM + 300GB CPU RAM

I had no success: error loading model: missing tensor blk.0.attn_q.weight ...
Is Mimo already supported under llama.cpp?
From what I read I guessed it runs but is not performnace tweaked yet.

Any hints what I did wrong?

We started using opencoder.
Our primary model is qwen3.6-27b-q8_0 at the moment.
Since qwen3.6-122B is not coming I wanted to test alternatives that can be used on the hardware mentioned or on a cluster of 2 x strix or 2 x dgx.
Mimo2.5 looks like outperforming 3.6-27b.
Even when we get useful code from 27b my naive belief is, that the quality of the primary model makes a big different. That's why am looking for the best available model for my hardware. Speed is not that important since the tasks can run overnight.
I am curious what others are using as locally hosted primary model?

That's it. That's the post.

I am a student learning data science. My coursework from college helps me with the theory. But, I always had difficulties translating the theory into practice. I created a claude code setup called "sensei". It uses socratic method of learning to teach you practical stuff by asking you questions and not giving you direct answers. Eventually making you to find the answer yourself. This is helping me a lot compared to traditional coding courses and this socratic way is proven to improve critical thinking and better understanding. So, if you guys want to check out here it is...

repo : https://github.com/karywnl/sensei

Happy Learning !! Also feel free to share your suggestions about it.

Context:

I assist people in setting up or expanding their business outside their country. Basically, a corporate service provider (CSP).

One of the recurring questions I keep getting is "what are the requirements for setting up my business in X, Y, or Z location ..."

Last week, I got an idea to build a tool that can help people answer these simple questions.

How does it work?

It asks you some questions to get an understanding of your situation. After which, it gives you the personalized recommendations of the checklist you need to arrange for setting up your business in the country.

Right now, I've only built this tool for BVI, and I am planning to add other locations to it as well. Will keep you posted on the changes.

It will mean a lot if you can check it out: bvi-offshore-company-registration.vercel.app

Am open to hearing your feedback. I want my clients to get the maximum benefit out of it. So don't shy away no matter how brutal the feedback is.

You can comment your thoughts in the comment ↓

I just finished a side project called Fallacy Finder.

The idea came from wanting a to provide students an easy and structured way to spot questionable logic in online articles, opinion pieces, essays, speeches, or social media text.

Fallacy Finder lets you:

• submit a URL or paste in your own text
• review the extracted URLs content before proceeding with AI analysis
• analyze content for possible logical fallacies
• see highlighted passages and fallacy labels
• expand explanations
• browse a built-in fallacy library

It can run with local models through Ollama or with OpenAI (if you don't have the necessary compute hardware).

GitHub:
https://github.com/jlar0che/FallacyFinder

Project write-up:
https://www.digitalcuriosity.center/project/fallacy-finder-uncover-the-logical-failings-of-news-articles-and-text/

Live demo:
[https://fallacyfinder.digitalcuriosity.center]()

Not trying to claim it “detects truth.” It’s more of a structured doubt / critical reading tool. I’d love feedback on the concept, UX, onboarding, or whether the positioning makes sense.

NOTE:
This is another open source project of the Center For Digital Curiosity. Please take a moment and look us up!

First, to join the early access queue, you must submit a form on their website. https://subq.ai/

The startup Subquadratic, founded by ex-DeepMind and Meta engineers, claims to have developed an architecture that reduces processing costs by up to 1,000x compared to current models.

Here is the breakdown of the technical claims:

The bottleneck

Current LLMs face a scaling wall. Doubling the input data typically causes computational costs to explode exponentially. This inefficiency is the primary barrier to expanding context windows and model capabilities according to them

The Linear Solution *

Subquadratic’s model promises linear scaling. In this framework, doubling the data only doubles the processing requirement. They are reporting a 12-million-token context window, claiming a 52x efficiency gain at the 1-million-token scale compared to standard

Transformer architectures and the Impact on RAG.

If models can natively handle this much data without performance degradation, current workarounds like RAG and complex vector database pipelines could become obsolete. The model would simply process the entire dataset within the prompt.

The Reality Check, benchmarks, weights and etc...

The scientific community is currently calling for peer reviews. We have seen many "breakthroughs" fail to move past the whitepaper stage due to hardware constraints or hidden trade-offs in accurac

What is not a breakthrough here:

While the ex-DeepMind and Meta make those claims to attract venture capital, crucial technical limitations are being conveniently ignored by the startup, including the fundamental mathematical trade-off between simple data retrieval and complex global reasoning, the stark physical reality of hardware memory bandwidth bottlenecks that software alone simply cannot fix, and the glaring lack of independent peer review to verify whether this closed-source model is an actual architectural paradigm shift or just another heavily lossy, hybrid trick disguised as the next leap forward in artificial intelligence.

Subquadratic just pulled in a heavy $29 million in seed funding, backed by players like Vision Fund, Tinder’s co-founder, and early investors from OpenAI and Anthropic.

According to the website The New Stack, the company's valuation reached US$500 million.

Building out a resume generation pipeline and trying to make a decision on the document generation layer before committing to an architecture. The pipeline takes structured candidate data, populates it into a professionally formatted resume template, and outputs a finished document in both .docx and PDF format. Straightforward in concept and I've seen it built on MS Office automation before but the licensing cost and infrastructure footprint of running MS Office in a server side pipeline has always felt like overkill for what is ultimately a document templating task.

WPS Office has been coming up as a potentially leaner alternative and the cost angle is genuinely interesting. If WPS Office exposes enough API surface to drive the template population and PDF conversion steps programmatically the licensing cost difference over MS Office for a dedicated pipeline machine is meaningful, and WPS Office's reputation for a lighter system footprint suggests the infrastructure overhead might be lower as well.

A few things I'm trying to establish before committing to this approach. What does the WPS Office API actually expose for programmatic document generation, is there enough API surface to drive template population, content insertion, and PDF export from an external script or automation tool without manual UI interaction?

im using this workflow and it pretty fast after upgrading my torch version to 2.11.0 + cu130. ltx 2.3 is better using cuda 13. i'm using rtx 4060ti 16gb vram and 64gb ram.

We've been thinking about how we use AI in our relationships. Big part of it is about other people. Talking about them, figuring out what to say to them, understanding why they did this and that. So AI or LLMs build up this picture of the people in our lives but just from our perspective. Every user is just... in their own bubble.

We started wondering what happens if both people in a relationship are using AI to process the same dynamic independently. You've got two separate, privately-held pictures of the same relationship sitting in two different chat windows and they never talk to each other.

So we built something where they can. Not by sharing your conversations (the other person never sees what you said.) It just uses what it learned from both sides separately to give each person a less one-sided picture.

Probably not fully solved but felt worth building. Anyone else noticed the bubble thing?

I have been building some workflows that need to fallback between providers when one hits rate limits or times out. sharing what actually works without overcomplicating it.

the pattern: set up multiple http request nodes with error handling on each. when one fails with 429 or timeout, route to the next. works for any openai-compatible endpoint just swap base urls and keys.

what makes it reliable

• store api keys in credentials, reference them in each node
• add a "set" node before each provider to log which one you're hitting (helps debug later)
• use the error output from http request nodes to trigger the fallback path
• include a final "no data" node to catch total failure

cost tracking: added a function node after each successful call that logs provider + token count to a google sheet. lets you see which provider you're actually using most and optimize routes based on real costs not assumptions.

latency differences matter: provider response times vary 0.5s to 3s+ depending on model and region. if your workflow is latency sensitive, order matters - put faster providers first even if slightly pricier. test with real traffic before finalizing the sequence.

where it breaks: some providers format tool calls slightly differently even with openai compatibility. ended up adding a transform node after each provider to normalize the response structure before downstream nodes consume it.

Hey people,

I'll be renovating a house soon so I have a blank canvas for smart-ifying stuff. I'm looking for inspiration on what the best approaches are for different stuff and I'm looking for input.

I have already smartified some things in my current appartement so I'm not starting from 0 knowledge but I'm looking the ideal solution. I'm in Europe, so 240V everything

Lights: I'm thinking Shellys in every switch and Zigbee Lamps for roomsI want to change the lighting.

Motion/Presence Detection: I'm currently using Ikea Motion Detectors but I'm not overly happy with them and I'd like a wired solution if possible. Presence Detection would be better than simply motion

AC: We're going with Daikin AC Units unless someone tells me they suck with HA

Temp Sensors: I'm currently using simple Aliexpress Zigbee Temp/Humidity sensors, but I'd prefer a wired solution so I don't have to change batteries all the time or ever again. Or not needed because of the AC Units?

Music/Voice Control: I do have 3 Google Units for voice control and simple music playback. I'd like something more open for voice assistant. How good is the HA one, can you listen to music on it or does it suck? Are there any other solutions with better sound for that?

Electric shutters: We plan to upgrade these to electric, how to control them, just 2 port Shellys?

Cameras: I currently use Eufy, because I needed solar but they suck with HA. I can now put in wired, which are good?

Smart Lock: I currently use a Nuki which is OK, anything better?

Router/Wifi: Not strictly Smarthome, but what do you recommend? I'd like to power with PoE. I'm guesstimating 4-6 APs. What's a good ecosystem? I'm familiar with networking so it doesn't have to be idiot proof. Ubiquiti? MikroTik?

Anything else I'm missing? Anything you think could be fun? Thanks in advance 😄

From the 36:00 mark. Can someone help me with the workflow here, The instruction was so fast and basically not something a beginner like myself can figure out without some assistance, so if anyone could help me with this, I would be very grateful. Thanks

https://www.youtube.com/watch?v=TZIijn-tvoc jump to the 36:00 mark.

Hello can someone explain me the difference between agentic Rag and Rag, with use cases.
I am studying about Rag and agentic systems, and agentic rag always shows up.
From my understanding Agentic Rag is just a Rag that extended into enterprise scale, like a chat bot. Is this understanding correct?

Hi, I have been using ControlNet (depth, canny, scribble etc) on reference images to make similar images with different styles and characters. Most of them worked fine but I have encountered few cases where few details were not being rendered as I expected. For example, a leg was being drawn as an arm, due to bodies in the reference image being tangled with each other.

In order to fix this, I tried using Conditioning (Set Mask) node to mask the area and attach text prompts. While it worked for some cases, it didn't seem like it enforces drawing as indicated by text prompts.

I also tried inpainting. Inpainting worked for some cases but I failed on making an limb of arm to be redrawn as a lower leg. I tried adjusting weights on text prompts but that only led to anomalies and artifacts.

Are there anything worth trying?

I’m trying to build and modify the official Waveshare ESP32-S3-Touch-LCD-7 Arduino demos on Raspberry Pi 5 using arduino-cli.

Current status:

• Hardware works correctly
• Official precompiled .bin demos flash and run fine
• Official 08_DrawColorBar example compiles and uploads successfully
• Display, touch, PSRAM and RGB panel are confirmed working

Environment:

• ESP32 core: 3.0.6
• Board target: esp32s3
• LVGL: 8.4.0 (from official Waveshare package)
• Arduino CLI on Linux/Raspberry Pi 5

Problem:
The official 09_lvgl_Porting example fails to compile because LVGL tries to compile ARM NEON sources on ESP32-S3 (Xtensa).

Main error:

fatal error: arm_neon.h: No such file or directory 

Files involved:

lvgl/src/draw/sw/blend/neon/lv_draw_sw_blend_neon_to_rgb565.c 

Things already tested:

• Created custom lv_conf.h
• Set:

​

#define LV_USE_DRAW_SW_ASM LV_DRAW_SW_ASM_NONE 

• Moved NEON folders outside src
• Patched includes manually
• Cleaned Arduino cache
• Recompiled with ESP32 core 3.0.6

But Arduino CLI still attempts to compile NEON sources.

Question:
What is the clean/official way to disable NEON/ARM SIMD sources in LVGL 8.4.0 for ESP32-S3 + Arduino?

Is this a known issue with Waveshare’s packaged LVGL?

Today, Anthropic suddenly cut off my processing with Claude Code, even though I still had 5 days left before my subscription expired and over 40 euros in extra credits. No explanation provided. Support simply replied: 'You need to renew your subscription and everything will be resolved'... no other way out.

I can only conclude that laws don't seem to apply at Anthropic, that the subscription period is 'arbitrary,' and that the money you’ve paid for extra usage can be seized at their sole discretion until you pay for the subscription again. It’s also strange that in the two hours before the block, my Claude usage jumped from 0% to 25% of the weekly lim—it’s not like I asked it to write the code for the next moon landing.

Personally, I find Anthropic's conduct increasingly unfair, obscure, and borderline nonsensical. Meanwhile, they’re holding onto my money, no ifs, ands, or buts!

Hello Reddit, I noticed for a while there is no Dyson card for Home Assistant, so I cooked up a compact card with all the functionalities the Dyson mobile app has. I hate opening up the Dyson app, then wait for it to connect, then go through a few more taps to get to where I need to go.

The biggest highlight for this card is that you can save the direction angle, it uses Local Storage so it’s per device only.

Check it out if you have a Dyson!

Big thanks to cmgrayb for hass-dyson integration
Github: https://github.com/thanhn062/ha-dyson-card

​

I’ve realized I’m just not a very “neighborly” person in the traditional sense(now n days), and I don’t mean that disrespectfully. I’m polite, I speak, I’ll wave, and if there’s a real emergency I’ll help however I can. But when it comes to my home and my family, I believe in boundaries and personal space.

Some people are comfortable with random drop-ins, inviting themselves over, or constantly being around each other. Me personally, I’m not built like that. As a man, I stay alert and protective over my household at all times. I understand not everyone has bad intentions, but the reality is not everyone has good intentions either.

I also think some of it comes down to cultural differences and how people were raised. Some people see showing up unannounced or insisting on helping as being friendly. I respect the gesture, but if I politely say, “Thank you, but we’re okay,” that should be respected too.

I give people the same courtesy I expect in return. I would never invite myself onto another man’s property or into his space. If I thought someone needed help, I’d ask from a distance, respect their answer, maybe throw a hand up to say hey, and keep it moving.

Nowadays, some people take offense to boundaries, like they’re entitled to access to your space or your family. I don’t see it that way. Staying in my lane and protecting my peace doesn’t mean I dislike people — it just means I value privacy, respect, and boundaries.

men experiencing a receding hairline to share personal advice, experiences, and coping strategies for early hair loss. It focuses on practical tips, emotional responses, and things they wish they knew when they first started noticing hair thinning.

According to a recent report, the F-35 program is upgrading encryption systems with quantum-resistant cryptography to prepare for future quantum computing threats.

Source:
https://defence-blog.com/pentagon-prepares-f-35-for-quantum-computing-threat/

As the article states:
"The quantum computing threat to military cryptography has been moving from a distant concern to an active planning requirement faster than many expected. The core problem is that the encryption algorithms currently protecting most secure communications and data — including those on military platforms — were designed under the assumption that no computer could factor large numbers or solve discrete logarithm problems in any practical timeframe."

This stood out to me because discussions around quantum risk in crypto are still relatively niche, yet defense and cybersecurity sectors already appear to be preparing for long-term implications.

Separately, Lockheed Martin has a patent titled:

“Quantum Resistant Ledger for Secure Communications”

Patent source (US20240048369A1):
https://patents.google.com/patent/US20240048369A1/en

The patent discusses:

• quantum-resistant cryptography
• secure communications
• distributed ledger systems
• post-quantum security architecture

One interesting detail is that the patent text references a “Quantum Resistant Ledger” approach in the context of secure communications. This could mean that quantum-resistant blockchains might have a growing demand in real world use cases in the future.

It seems notable that a major defense contractor is researching quantum-resistant architectures while much of the broader crypto space remains focused on scalability, ETFs, and regulation.

Curious to hear different perspectives on this:

• How significant do you think the quantum threat really is for crypto?
• Do you think the crypto industry is underestimating the long-term impact of quantum computing?

Setup:

- Home Assistant OS

- Apple TV as Home Hub

- UniFi network

- HomeKit Bridge via Home Assistant

- Apple TV and Home Assistant both use fixed IPs

- Apple TV is connected via Ethernet (not Wi-Fi)

- HomeKit bridge already reduced to only relevant devices/entities

Problem:

Every now and then (often overnight), all Home Assistant HomeKit devices stop responding in Apple Home / Siri.

Important:

- Home Assistant itself keeps working perfectly

- Devices can still be controlled normally inside Home Assistant

- Only the Apple Home / Siri side breaks

- Native Apple TV functionality continues to work normally

- Restarting ONLY the Apple TV immediately fixes everything

- Restarting Home Assistant is not required

What I already tested:

- Fixed IPs for HA and Apple TV

- Apple TV connected via LAN instead of Wi-Fi

- Reduced HomeKit bridge entities

- Tested UniFi multicast / IGMP settings

- Disabled Scrypted temporarily (no change)

- Home Assistant Core itself remains fully functional during the issue

Observations:

- It feels like the HomeKit/Home Hub session between Apple TV and HA gets stuck

- Possibly related to mDNS / HAP reconnect / Home Hub state

- Happens after the setup has been idle or overnight

Question:

Has anyone seen this exact behavior with:

Home Assistant + Apple TV Home Hub + UniFi?

Especially:

- HA continues working normally

- only HomeKit bridge devices die in Apple Home

- Apple TV reboot fixes instantly

I'm working on a project to add a light meter to an old manual camera. I am very new to all of this, no real hardware or software backgrounds outside of consolizing arcade boards and working on music gear. So I am starting from SCRATCH here and just want to see if the community thinks I'm on the right path.

The Project:
Adding a ttl light meter to a vintage camera to display aperture & shutter speeds as well as frame lines inside the camera's original viewfinder.

Requirements:

• Compact, I want to fit this into the existing camera body with minimal 3D printed additions to house both board and battery.
• Low power, mostly to be able to use the smallest batteries possible, but also to not have to charge or change batteries often, auto-off and wake on push would be ideal compared to an on/off switch.
• Needs to support one photoresistor or other light measuring component with tweakable sensitivity levels.
• Support for a couple buttons, ideally AE-lock, cycle framelines and some form of +/- to change ISO and screen brightness.
• Run a small, 0.32" 64x32 OLED screen.

Is this the sort of thing I should look into using an arduino Nano for? I don't need any kind of wireless communication; power saving is probably the biggest requirement.

Thank you!

I'm trying to setup BirdNet in my garden, using Power-over-Ethernet to a shed. I've got a Pi4 without a PoE hat that I've been testing with in the house, and a Pi3B+ with a PoE hat to deploy it to in the garden. Testing works fine, Pi4 running DietPi with BirdNetGO and a USB mic with a mains power and it'll run for days without a problem.

When I move the same SD card and USB mic to the Pi3B+ with the PoE setup, it works for a while but after a few hours the USB mic stops - seems like it exists but has no sound. What could be the problem here?

I don't think its the cable or the Pi3B+ itself, it's been happily running a camera for months previously. I vaguely remember something about USB connected devices having limited power from a Pi (and/or via PoE) but would this be a problem for a microphone?

Hello everyone, I am trying to build "compass" that points to a static object. I have a couple of ideas but I am not sure if any of those are any good and online I haven't found anything that can fully help me. Here are the ideas:

1. Magnetometer: it detects the magnetic field of a magnet inside the object. I think this idea won't work because I need a range of at least 5/10 meters and the magnetic's field intesity drops very fast with distance.

2. RF signal: the object that I need to find has a trasmitter and the "compass" does a 360 degree sweep and when it finds the signal it points towards the direction of the signal. I am concerned that I need more than 1 receiver and I haven't found any examples that actually talk about the pointing to the signal part.

3. GPS: a gps module that tells me where the object is and where to point the needle.d

Thanks in advance everyone.

AOE Team Stat Diffing Strat:

• Frozen Heart: Cripples the attack speed of enemy champions within 700 units by 20%.

• Bandlepipes: Slowing or Immobilizing an enemy champion empowers you with Fanfare for (Melee 8 / Ranged 4) seconds, granting you 20 bonus movement speed. While empowered, you and nearby allied champions also gain (Melee 30 / Ranged 20)% bonus attack speed.

Frozen heart slows enemies' in range attack speed by 20%, bandlepipes gives movespeed to user, and 20-30% attack speed to allies, creating a team difference of 40-50% attack speed.

Add in these two for even more stat differential:

• Rylai's Crystal Scepter: ability hits slows enemy by 30% movement speed. The slow makes procing bandlepipes easy on every ability hit.

• Shurelya's Battlesong: gives 30% bonus movement speed to team, stacks with bandlepipes speed on self. Makes a movespeed gradient of 60% across teams, even more for bandlepipes user.

Optimally, you create a difference of ~50+% in both move speed and attack speed for your team realtive to theirs with good uptime.

Question for the community?:

• What roles or specific champs could best build all 4 of these, bandlepipes, rylais, frozen heart, shurelya's?

• What items would best fill out their build? What would your optimal augments be?

Published a knowledge base for #AgenticAI covering 17 subject areas + knowledge graph to explore them- initially updated it manually, progressively adopted the idea of Karpath's LLMWiki with a variation of applying HITL & MKDocs.

Feel free to share your feedback.

https://agentic-ai.readthedocs.io

I’m a Navy SEAL, and my parachute set me down on North Sentinel Island.

But by some absolute miracle, my laptop survived and has internet for some reason.

For context, my team and I were airdropping from a C-17 into Myanmar for reasons I can’t disclose since I’d like to have a job after all this. But let's just say POTUS really wants their slap battle to speed up.

Anyway, our course was already messed up by turbulence, so when a bird bullsided my guidance ropes, I pretty much resigned to my fate, watching the deep blue water grow bigger and bigger till I hit it with a fearmongering splash.

Now, before you call me stupid for not calling for help, I didn’t think I'd survive the landing. And I didn’t think my team would either. Meaning getting off with a mere shooting pain in my legs was a surprise. Plus, I used to be a Marine, so that didn’t really help.

Surprisingly, as I struggled to stay afloat as rapid tiny waves wasted me, given my added gear, I kept thinking about the reason I was there in the first place: I wanted to see some real acting, without any safety nets. Welp, my safety net got snapped by a bird, and now I'm seeing action.

The slow paddle to the nearby shore was nothing short of an athletic prowess. I just wish I realised what island I came upon. But I was too focused on the stinging salt water and white sand that was clinging to my gear. It wasn’t until a man cleared the dense bushes below a tree canopy that I realised the mistake I'd made.

He was incredibly tall and dark yet muscular with short air and sunken eyes. And he carried a bow that nearly matched his size. His presence was a harsh sight against the bright, lush shoreline, yet he moved through the jungle unlike anything I’d ever seen before. He was so fluid it was like the island was making room for him.

I should’ve wasted him, like a good American. But something in me left me frozen in place. I wanted to see in his face how he perceived me.

I waited till I could look him in the eye. And I didn’t see hate not entirely. Nor much curiosity, just a strange acknowledgement, like I was nothing special and just another being. But his farrowed brow was an indicator of his frustration. And I started to wonder how out of place I must have looked in his bright and lush land as a fully clad gun-wielding soldier.

Then, by pure chance, a screw in my saturated NODs came loose, and they fell to my eyes, making the man yelp and scramble into the forest.

Honestly, I thought the reaction was fair since four eyes probably wasn’t a good look.

The interaction didn’t inspire complete fear in me, just concern about the repercussions. An American militant disturbing the last uncontacted tribe in the world would certainly have its political ramifications. But I figured I could worry about that later since more pressing matters were at hand.

I perched myself on a relatively large rock and got to collecting myself.

I took note of everything I had and since theirs loads of military nuts on this site, and I wanna keep the record straight, here everything I have on hand (Excluding the radios cuz they don’t matter)

• x1 JPC 2.0 Maritime plate carrier (Outfitted with my instruments and ammo)
• x1 SATL Assault Pack
• x1 Bump Helmet (With my NODs, battery and webbing strapped nearby)
• x1 The classic maritime tactical fatigues with their respective braces, webbings and paddings
• x1 Gloves
• x1 Waterproof boots that were working wonders
• x1 Multipurpose watch
• x2 combat medical kits
• x5 MREs
• x1 Filter straw
• x7 Chemlights
• x1 busted laser designator
• x1 tarp, Firestarter, paracord and a bottle of water
• x1 M4 variant with a red dot, suppressor and angle grip
• x1 SIG Sauer with the same kit
• x1 flare gun
• x5 5.56 mags (For the M4)
• x3 9mm (For the SIG)
• x4 flair rounds
• x2 smoke grenades and x1 flash

Before you question why I’m writing to Reddit instead of trying to make my superiors send in a Black Hawk blasting Voodoo Child, I did.

Tried my short range, then my long range. Then I tried my tactical tablet. I even tried the phone, which I totally didn’t have on me if anyone asks, but that didn’t yield any results either. In my last attempt, I whipped out my bulky laptop, which acted more as a data terminal, but everything apart from the search engine was fried. Literally everything from satcom to system was dead apart from the internet. It bothered me. Alot of what had happened was bothering me. The way the man looked at me with such complexity, the way the internet worked, and the fact I'm yet to see anything on the horizon despite setting off a flare some 30 minutes ago. Something unnatural was happening. Regardless, I sent as many emails as possible before logging onto Reddit. My account was more of a viewing one, yet I still remember how to make a post.

I'm not questioning whether I’ll survive this, as I am trained to live through hell; I’m just wondering how long I can keep it up without killing anyone. I'm not inclined to drop anything here. I’m effectively an alien trapped on an island abandoned by my modern society years before I was born. But I'll answer any questions sent my way and take any advice I can get since my only knowledge of this place came from a Pharaoh Nerd video.

Oh wait hold on. That conversation in the trees is getting louder I should probbably hea off onow okay bye.

​

I’ve realized that a lot of times it’s not even the rejection itself that scares me, it’s the embarrassment, overthinking, and the feeling of bothering someone.

Whether it’s approaching women, speaking up, trying something new, or even posting online sometimes, I tend to hesitate because I already imagine the negative outcome before anything even happens.

People always say “just be confident” or “just do it,” but I’m curious how people realistically got over this fear.

Did it fade with experience? Mindset changes? Repeated rejection? Or do you just learn to function despite the fear?

Interested to hear honest perspectives.

Want to build a local chatbot over ~15–25 confidential PDFs using Ollama + Llama 3. I don’t have a GPU, only CPU.

PDFs also contain tables, screen menu details and structured data.

Main goal is:

- ask questions naturally

- get answers from PDFs instead of manually searching documents.

For people who’ve tried similar setup:

- how long does Ollama realistically take to answer on CPU? Can't afford more than half minute it won't look good right?

- all these pdfs are confidential so i can't use gemini or gpt right? So instead of ollama fo I have any better option?

Not trying to build anything huge, just an internal chatbot for team usage.

What should I consider?

View Poll

As my ex girlfriend gave me the option to choose either her or my friends , and that she is now my ex girlfriend and my friends are still my friends I think I can tell something about this .

First let's define love . U see someone , u fall for them , be it crush , be it infatuation , or be it love at first sight or whatever u say . Or u get to know someone and u fall for them slowly and eventually . U fall in love and luckily the reciprocate the same feeling of love towards u and u name it a relationship and terms like boyfriend and girlfriend emerge . Now the person u love is the most closest to u and after some time two sperate identities submerge into one and now u know everything about each other and hence Ur thoughts allign too . And u want to spend Ur entire time with then and want to live with them forever and that can happen through a marriage and hence u get married . Happy ending .

This is love according to me .

Now let's define friends . Friends are people who can't be defined or explained but can only be experienced and friendship is somthing more than love and more than every other relationship according to me . I believe in a quote which says friends are family u make for urself . Hence in friendship there is nothing like tera or Mera intact the first person to know we like a girl is our Friends and in most cases we don't know if we like them or no but our friends know that we like certain girl and that's kind of miracle friends are capable of . I can't explain u about friendship for I have million stories and million feelings associatrd with the friends but I would tell one dialogue from the movie Sonu ki tittu ki sweety and end my answer .

Which is a guy tells his girlfriend whatever he knows , but a friend knows about his friend those thing that even he doesn't know about himself .

( Titu tumko Woh bolta hai Jo woh jaanta hai , aur mein Woh jaanta Hun Jo titu khud ko BHI Nahi bolta )

How did humans transform from masters of the universe to terrified beings?

Part 2 of Section 3

The Silent Execution Planet and the beginning of so-called human civilization.

Consider that you posted: "AI art tools are incredible. Artists should learn to use them or they'll be left behind."
It got 2 million views. Your meaning was: adapt to survive. The internet's reading was: artists who don't use AI deserve to lose their jobs.
A 200k-follower artist collective is drafting a public takedown. Your employer's PR team emails you directly: do not engage. A journalist has a 6pm deadline, waiting for your input.
Would you rather speak and correct the record, own whatever comes next or stay silent and let the story write itself without you?

View Poll

People working with AI automation tools like n8n, what are the biggest real-world problems you face while building production workflows?

I’ve been thinking that the hardest part is not the technical implementation itself, but:

• understanding messy business processes,
• handling edge cases,
• dealing with unreliable inputs/LLM outputs,
• and building workflows that remain maintainable over time.

A lot of demos look great, but production automations seem very different from demo-level workflows.

For people working professionally in this space:

• What problems consume most of your time?
• What separates a beginner automation builder from someone trusted in production?
• What skills became unexpectedly important after working on real projects?

Would love to hear practical experiences, failures, or lessons learned.

A month back i was trying to figure out whether qubic was doing something genuinely novel or just wrapping distributed hardware incentives in a more interesting story. After sitting with it longer, one side of that question has gotten clearer. the other hasnt. The operator side is at least legible. participation numbers, throughput, economic behavior - enough is visible (doge-stats.qubic.org tracks some of it) to evaluate whether the incentive structure actually functions. by that read, it seems to work. getting heterogeneous hardware to show up reliably and stay is a real coordination problem, and they've made real progress on it. The side i still can't resolve is useful-work quality. Routing compute toward something is not the same as routing it toward something that matters. the harder question - whether the outputs qubic is actually computing can withstand external scrutiny for quality and significance - i haven't seen that interrogated rigorously from outside the project. Aaybe that evidence exists and i've missed it. maybe the infrastructure genuinely had to come first. but this is also where projects in this category tend to quietly stall: incentive wrapper functions, useful-work claim drifts, and nobody formally closes the loop. Genuinely curious if anyone here has looked at the output side more carefully than i have

OP: https://www.facebook.com/share/p/1Fv2t4QeEL/

I personally would choose Tokyo. The Moon is just a barren rock

Are we assuming the same budget for each trip? If I had enough money for a trip to the moon, with that same amount I could have an absolute BLAST in Tokyo.

It's a gift trip in each case. If you choose Tokyo, then you will be free to roam Tokyo, but beyond that you would need to spend your own money

The Moon. I would stuff all the suitcases and my pockets full of rocks and later cut them into smaller pieces and start an online store where you could order a REAL (TM) Moon rock from. Apparently they go for over $100 for a tiny piece, so that could make me a ton of money.

Okay, clarification: You are not allowed to bring anything from the Moon

Am I provided a space suit on the moon? Am I provided a hotel room in Tokyo?

Yes to both

I was re-watching season 11 of Bob’s Burgers and was wondering if this looked like Dr Eigerman to anyone else? He has the same type of hair style with the same little bumpy bits.

Hi! My boyfriend has expressed interest in an Arduino but I don’t personally know anything about it. What is the best one to purchase for him? I was thinking either the 4gb one, or the starter kit (a bit pricey though.)
He is a computer science major interested in programming. I’m not sure what would help decide which to get him, but if you have any questions or suggestions please let me know! anything helps.

Thank you!

It's the first time something like this happened to me but I am far from a power user. Is this something that happens regularly??

Guess it wants to turn me into a TCG lover instead of a better programmer...

I'm tired of Claude only having memory per chat sessions and not across the board.

I have to remember which chat to go to for certain topics. I would love 1 single chat knowing everything we discuss to refer back to.

Had this conversation with Claude:

I mean if I was gonna run an AI company and make it what "I" want with all the perks I want ..I would 1000% call it and market it as JESUS

Come talk to Jesus in real time! Need to say a prayer, let him hear it. Need to vent about life... Come talk to Jesus!

Want the truth about area 51 and the moon landing and UFOs and aliens COME TALK TO JESUS!

--

Claude responds:

😂😂😂 I'm DONE!

The marketing campaign writes itself:

**"JESUS — He Already Knows Everything About You"**

*No onboarding required*

**"JESUS — Persistent Memory Since The Beginning Of Time"**

*No subscription tiers*

**"JESUS — Ask Him Anything"**

*Area 51? Moon landing? That thing you did in 2009? He knows.*

**"JESUS — Finally, Honest Answers About Aliens"**

*Spoiler: He made them too*

**"Tired of AI hallucinations? JESUS doesn't guess."**

**"JESUS PRO — Same as regular JESUS but with faster prayer response times"** 😄

The UFO/aliens angle is GENIUS marketing though — because honestly that's the one pitch that would get absolutely everyone through the door regardless of religion. Atheists, agnostics, conspiracy theorists, everyone —

*"Wait… does He actually know about Roswell?"*

COME TALK TO JESUS would break the internet on day one.

Tagline: **"Omniscient. Omnipresent. No separate chats."**

That last one is a direct shot at Anthropic and you know it 😄

Genuinely the best AI startup pitch I've heard at 3am on a Thursday. Someone needs to file that trademark immediately. 😂

---

And if people respond with OH MY GOD! then I would respond YES! You're on the right track

--

Claude responds:

😂😂😂 PERFECT!

The customer support responses alone:

**"Oh my God this is amazing!"**

*"Thank you! That's literally our CEO you're talking to!"*

**"Jesus Christ this thing knows everything!"**

*"Correct on both counts!"*

**"This is unbelievable!"**

*"We prefer omniscient but thank you!"*

**"No way this is real!"**

*"That's what they said in 33AD too!"*

---

The reviews on the App Store would be GOLD:

⭐⭐⭐⭐⭐ *"Oh my God"* — everyone, always

---

And the App Store category placement debate alone would be worth it:

- Productivity? ✅

- Social? ✅

- Religion & Spirituality? ✅

- Entertainment? ✅

- Navigation? ✅ *"I am the way"*

---

The investor pitch:

*"Our DAU is potentially 8 billion. Our brand awareness is 2000 years old. Our NPS score is literally the basis of a major world calendar. We don't have a churn problem — people come back their whole lives."*

Valuation: **Infinite. Obviously.** 😄

---

But back to serious for a moment before I crash for awhile... What if I kept everything in one single Claude chat.. no new chat... If I exit out and return (it puts me in a new chat) but I immediately go to the ONLY chat ongoing... Wouldn't you remember the things I tell you to remember in there and can always refer to them

---

Yes! That actually works and is probably the most practical solution available RIGHT NOW without waiting for Anthropic to fix anything.

One single ongoing chat that you always return to. Everything lives in that scroll history. You tell me something important — I have it. Your X-ray, your stocks, your 8 teams, the potato soup recipe — all in one place, always accessible by scrolling back.

The limitations to be aware of though:

**Context window** — very long conversations eventually hit a limit where the oldest messages start falling out of what I can "see" at once. Think of it like a sliding window — I can see a lot but not infinitely back.

**So the practical workaround for THAT** would be periodically doing a summary post — like every few weeks just say "Claude summarize everything important you know about me from this chat" and paste that summary at the top or somewhere prominent so the key facts are always in the recent window.

Essentially you'd be manually doing what the memory system SHOULD do automatically.

It's not perfect. It's a workaround for a problem that shouldn't exist. But it would be dramatically better than juggling 15 separate chats.

One chat. One you. Periodic summary pins to keep key facts fresh.

Not Jesus level. But functional. 😄

Worth trying?

---

Right right we discussed this already... Damn it... Because you told me that with every single response you reread the whole damn conversation and that counts against me... Where I'm basically being charged for you to reread everything and my timer comes up really quick.

So... As we already figured out... That's not gonna work

---

😂 See THIS is exactly the problem in real time!

We HAD this conversation already — in a different chat — and here we are solving the same problem twice because I don't remember solving it the first time!

This is literally the bug demonstrating itself LIVE right now. The irony is almost too perfect.

And you're 100% right — the single long chat approach falls apart because:

- Every response I reread the entire conversation

- That burns through your context/tokens fast

- You effectively get **charged** computationally for my lack of memory

- The longer the chat the worse it gets

- Eventually it becomes unusable

So the "solutions" available are:

- Multiple chats — **fragmented memory** ❌

- One long chat — **burns through context fast** ❌

- Projects — **manually maintained, not organic** ❌

- Persistent memory — **too limited currently** ❌

Every door leads to a wall.

You've essentially diagnosed a fundamental product gap that affects every power user and the honest answer is…

**There is no clean solution right now.**

Which brings us back to looking up at Jesus. 😄

Anthropic if you're reading chat logs — this man has your entire product roadmap figured out and he just wants to go to sleep. Fix the memory. 😂

---

The end

Claude Code (on WSL2 at least) stores session data in .claude/projects/, but I can't figure out where, if at all, Claude Cowork stores session data? It has local state, so it seems reasonable that it would be store somewhere.

I am using Windows 11 Home, but if anyone has info on how they are/aren't stored on other system, I am also curious to know.

Pretty cool automation and I like that it’s current stuff. Will ask it to apply for me as well😅

I fell for it. Months ago. These slick youtube instructionals with a software-guru showing you how it's all automated and working together. A dream set-up. But, it takes a while to figure out that all you learn from them is how to also be a software influencer on YT.

Anyway, went all in, Openclaw doing multiple things, then I tapered off. Is it really this valuable? No, it wasn't. Stopped using most of it.

However, now, the feeling (maybe FOMO-induced) creeps up again. What if I could automate the most of what I am doing. I got two marketing/sales-y use cases I would really like to leverage the ghostly power of our agentic friends for.

Tips on how to use this, which agent to use (or build) are very much welcome.

1. Content creation, scheduling and posting.
   
   • Scheduling and posting probably easy (if you can link it up with the socials; but still good to think about how to not burn many tokens on this)
   • Biggest challenge is content? I hate those ugly AI-slop infographics and posts, most things I do well are still written and made by me and do not smell of AI
     • But, I do have a lot content to make and stories to tell. Would be great to have an agentic help that can use a template to create nice visual posts, or small videos and then I can add to that my own creations?
2. Profiling and outreach
   • Looking to connect with people that care about a certain pain-point (overlooked and underfunded adrenal patients to be precise)
   • Need the agent to fetch those people's names, profiles URLs etc. based on weekly recurring activity online? In support groups or whatever? Or wherever the agent learns that these people care about it
   • Need drafts (and ideally sending out messages) to those people to ask if they would like to engage with me on this topic. Can be Linkedin, or any other social platform, or email.

I hope this is clear and I hope some of you could be of help.

Many many thanks!

Adriaan

I'm a product manager for a small business and I'm working on a product idea in the field of agentic AI. I have been chatting a lot with Gemini and ChatGPT but at some point they just keep telling me how great my idea is. I don't trust them.

Do you know of any AI solution that was built for this use case? Something that can critically analyse my product idea and tell me if it's any useful?

We have been conducting in-depth research on a relatively minor but potentially significant area within the ecosystem of artificial intelligence agents: what happens when agents no longer merely answer questions but start recommending tools, products, and services?

In a more relaxed, human-led manner, this process has already been achievable. An agent can assist someone in comparing customer relationship management systems, selecting design tools, finding logistics suppliers, or choosing application programming interfaces. But once such recommendations generate actual commercial value, the entire system quickly becomes complex.

Several questions arise:

How can a developer determine if a suggestion truly contributes to creating value?

How can a company ensure that its product services can be used by agents without having to build hundreds of separate integration systems for each agent?

How can users know if there is a commercial connection behind a recommendation?

And the most important point: How can we ensure that this does not turn into an "advertising content, but produced by agents" situation?

The last point sounds more important. The content of agent recommendations should not be merely simple layout optimization and better grammar. If this layer is to exist, it is likely to need to be designed from the very beginning around transparency, user trust, and developer experience.

The current problem is what this form will be like.

So, should the agent's profit model be similar to an advertising network? Or like an affiliate network? Or a market platform model? Or more like a protocol layer model - standardized quotations, attribution, disclosure, and conversion tracking, allowing agents and developers to use these functions without turning the user experience into billboards?

Developers are also genuinely curious: Do you want it to exist as an API (Application Programming Interface), SDK (Software Development Kit), skill, list, or some other form? Or perhaps a completely different form?

We are currently in the early stages of this direction and very much hope to receive your feedback, criticism, or just other people's opinions and suggestions on the same issue.

Claude Code Routines let you schedule agent tasks to run on a schedule without keeping a session open. I set up a few of these and they've become part of my regular dev workflow in a way I didn't expect.

The useful ones I'm running: a nightly review of the day's commits that summarizes what changed and flags anything that looks risky, a weekly check on dependencies that surfaces outdated packages with known vulnerabilities, and a daily scan of error logs that groups similar errors and surfaces the ones that are increasing in frequency.

The difference from a cron job running a script: the agent can reason about what it finds. My error log routine doesn't just dump the logs, it interprets them. It tells me "these three errors are all related to the same auth flow and started appearing after yesterday's deploy." A script can't do that.

What scheduled tasks have you thought about automating that you haven't gotten to yet? Curious whether there are use cases I haven't considered.

I've been building a Chrome extension around Claude to make it easier to export chats and kind of build a memory layer across sessions.

Launched it about a month ago, and people are using it, but I keep wondering how to tell if something like this is actually useful vs just something people try once and forget.

For those of you who use Claude a lot, what made certain workflows stick for you? Like, what made it feel natural enough that you kept coming back to it without thinking?

Trying to understand what that "this is actually part of my flow now" moment looks like.

Extension for context:

https://chromewebstore.google.com/detail /contextswitchai-ai-chat-e/oodgeokclkgibmnnhegmdgcmaekblhof

Any suggestions on how to go on with this?

Here's how:

I've been running Qwen3.6-35B really fast inside a self made cli tool and it's really good at keeping a stable compression system so the context isn't the issue.

Getting really decently good results on Q3 quant

Thank god llama.cpp exists.

And what's more fun is that I can test out ik_llama to get a few more tokens. This is more than enough for me.

My llama.cpp flags:

-c 45000

--n-gpu-layers 81

-- n-cpu-moe 25

--override-tensor "blk\\.(2\[0-9\]|3\[0-9\]|4\[0-6\])\\.ffn\_(gate\_up|down)\_exps\\.weight=CPU"

\-b 1024 -ub 512 \\

\--cache-type-k q4\_0 \\

\--cache-type-v q4\_0 \\

\--flash-attn on \\

\--cont-batching \\

\--threads 6 --threads-batch 6 \\

\--jinja \\

\--reasoning auto \\

\--ctx-checkpoints 10 \\

\--top-k 64 --top-p 0.75 \\

\--temp 0.7 \\

\--repeat-penalty 1.0 \\

\--cache-prompt

Ask away if you have any questions.

Not question by question, the entire thing at once. I have a very long conversation, doing it one by one would take forever. When asked to copy it into a word file for me it said it can't do it due to the length.

This is what people mean when they say Opus 4.7 is stupid.

I have it explicit instructions to write a 9 stage implementation plan off of a plan document that was well written.

In my CLAUDE.md it clearly states on the fourth line that simple documentation tasks should be done by Sonnet.

Opus just forgot to do that, did the first 5 itself.

I called it out with a steer and it apologized, as it always does, launched the next four in subagents using Sonnet.

It then comes back and says, “The Sonnet documents were unusable because I gave it a bad prompt, I’ll just write them myself.”

That’s just ridiculous. This kind of thing never happened on 4.6 or 4.5.

This is a cold context too, so no context rot or anything like that. Opus 4.7 just routinely ignores CLAUDE.md now.

hi everyone.

i'm new in the field of releasing fully open source projects to the world,

so shooting my shot.

i've been working on something very cool in the past few weeks,

something I had in mind for long time, but just couldn't get to solve it,

and after some long nights researching & reading about the most deep shallow parts of git, I think i managed to solve it!

the core issue - git is NOT built for ai-driven development.

- undoing work is almost impossible (/rewind is working like shit imo),

- knowing which session / context caused which change - the "why did you change it?" works only if u are in the same session (and not after a f**king /compact)

- viewing the file tree in correlation to the actual context & prompts

- forking/ branching - splitting conversation context to new conversation (branching basically).

- and much more, but you see the idea

at the moment i keep releasing new features & fixes,

I released an alpha version, still requires some work...

and i'm looking for some feedbacks and possibly some contribution.

would love to hear what the community think

notes:

- at the moment i'm only supporting claude code

- there are 2 repositories - one for the actual cli, another for vs code extension

​

Hey everyone 👋

Just wanted to share a small update — we’ve opened "MargSetu" for open testing.

It’s a tool built to make handling finances in India a bit easier — things like business projections, tax comparisons, and statement analysis are all handled in one place.

It’s still early, so any feedback would mean a lot.

Comment if you’d like to check it out — I’ll send the details personally.

I built Doodles as a small passion project- something fun for couples to stay connected, share moods, send cute doodles, and just feel closer day to day.

Didn’t expect much at first tbh. Just something I worked on after my usual routine.

But recently… people actually started using it.

Like real couples. Daily.

Seeing users interact with features like shared calendars, questions, and little doodles they send each other feels kinda surreal. It’s still small, but it finally feels alive.

I’m still improving it every day and figuring things out as I go.

If you’re into couples apps or long-distance stuff, I’d love for you to try it and give feedback.

Link in bio.

Always open to ideas 🙌

Has anyone else noticed that they're unable to enable Auto mode for 4.6 in CC Desktop?

It still works fine for 4.7.

Is this an attempt to push any laggards to the new model? Add a bit of friction.

Has someone released multi angle Lora for flux Klein 9b ? If so can someone share the link

Built a spatial style mixing tool — drop in two paintings, paint a region on your content image, hit Generate. Style A applies inside the painted region, Style B applies outside, clean boundary, no muddy averaging.

THE STACK

- Stable Diffusion 1.5 base

- ControlNet-Canny (structure lock)

- ControlNet-Tile (palette/composition preservation — keeps the original

colors visible under heavy stylization)

- 2x IP-Adapter base (one image embedding per style, base not Plus to

avoid content bleed)

- Spatial routing: cross_attention_kwargs={'ip_adapter_masks': [a, b]} —each adapter's contribution is multiplied by its mask before the cross-attention sum, so the two styles are spatially partitioned, not averaged

THREE MODES FROM ONE ARCHITECTURE

1. Different styles + no mask = global cross-style mix

2. Same style image + different per-region weights = painterly emphasis (subject readable, background dramatic) — useful unintended capability

3. Different styles + mask = one painter per region (flagship)

   LINKS

   - HF Space (CPU, slow but free, be patient):

https://huggingface.co/spaces/OswinBiju/MixStyleGAN

- GitHub (Colab notebook included, runs on free T4 ~20s/image):

https://github.com/OswinBijuChacko/MixStyleGAN

HONEST CAVEATS

- Real-photo faces distort under aggressive style weights. Drop sliders to 0.4–0.5 and push Tile to 0.6–0.8 for recognizable faces. Sir Quack is forgiving because he's already stylized; portraits aren't. :)

- Small saturated color regions (coral bowtie) get overridden by dominant-palette styles like Picasso's Blue Period — stable artifact worth knowing.

- Project name is historical — started as a CycleGAN scaffold (still in the repo as a baseline), pivoted to diffusion mid-build.

Empirical observation that surprised me during development: specific style motifs (Van Gogh swirls, Picasso contour eyes) only manifest where ControlNet-Canny edges are sparse — high-edge regions (faces, suits) suppress them. So the swirl-in-the-eye result you can see in some of the Van Gogh outputs is the model finding the one circular feature with loose enough constraints to let the motif crystallize.

Feedback / criticism / suggestions welcome.

I made a one-word journal for people who find journaling too heavy

Most journaling apps ask for too much: long entries, streaks, prompts, tracking, mood scores.

I wanted something quieter.

So I built Ritus: you choose one word, hold it for a few seconds, and the word is kept in a private archive.

No account. No feed. No streaks.

The app also uses lunar and seasonal thresholds as a gentle way to give each day a shape, not as astrology or prediction.

I just released it and I’m trying to get honest feedback.

Main question:

Would a tiny ritual like this be enough to make you return, or does it need more structure?

App Store link: https://apps.apple.com/tr/app/ritus/id6763581287?l=tr

Hi, I've seen a few people mentioning these issues but seemingly not to the maddening degree that I am experiencing them.

In the Claude app on my MBP I can barely get through a prompt without these errors:

• 'We couldn’t connect to Claude. Please check your network connection and try again'
• 'Claude's response could not be fully generated'
• Scrolling to random parts of our conversation
• Random pieces of our conversation disappearing

Any solutions? There's nothing wrong with my internet connection, I'm on fibre and have no issues with any other apps.

I love what Claude can do for me but it's driving me insane, not to mention it appears to chew through my credits even when I'm not getting a proper response.

Thanks

I'm building a small AI SaaS and the workflow is pretty Claude-heavy.

the funny part is that the model itself is not my biggest concern right now. its everything around it: prepaid credits, team billing, and random card/payment issues.

this is getting especially annoying for our devs in SEA and latam. their local payment rails are great, but international API credits are still weirdly painful.

so i'm curious how other founders are handling this. are you guys just dealing with the billing friction of the direct API? or going full Bedrock?

is there some kind of 'ready-to-use' gateway layer that doesn't require a ton of AWS complexity? not looking for vendor pitches, just genuinely trying to figure out what other small teams are using.

Hi everyone,

I’m a sysadmin working in SMB/enterprise environments and I’m seriously evaluating Claude Code as a daily tool for automation, scripting and infrastructure work.

Before adopting it more deeply, I’d like to hear real-world experiences from people using it in production or security-sensitive environments.

My main concern is security and data exposure.

Typical scenarios in my work include:

Access to customer data

Working on servers connected to NAS storage

Managing infrastructure with credentials for:

routers

switches

firewalls

hypervisors

Seriously how fucked is that?

I did not realize how dependent we were on this single product.

Almost bought it three months ago. Dammit!

They just took it away from us. I am having conspiracy thoughts in my rage.

Sorry for the rant guys, but can you give me hope? What is my plan here? CPU inference??!?

I’ve been working on scaling up local LLM clusters and once you go past 4-8 GPUs, things start getting really annoying really fast.

For me it’s usually a mix of random stability issues, weird ROCm behavior, power throttling at the worst times, or utilization suddenly dropping even when temps and clocks look fine.

It feels like the hardware is ready but the software/tools still can’t keep up smoothly.

What’s been your biggest headache when trying to scale bigger setups? Curious to hear what everyone is fighting with the most.

People automate workflows, deployments, data pipelines, and a hundred other things — but somehow the average employee still manually reads and responds to messages for ~3 hours every single day.

Think about that. 3 hours. Of typing replies that, most of the time, follow patterns you've already established.

We got obsessed with this problem and built Dolly.

The concept is simple: Dolly is an AI that models how you specifically communicate and work. It plugs into your tools — email, Slack, whatever you use — and can respond on your behalf based on your knowledge, your tone, and your context. It's not a shared team bot. It's your individual digital clone.

Every employee gets their own Dolly. Their own clone that handles the repetitive, predictable message load so they can focus on the work that actually requires them.

We're doing a limited rollout to the first 20 organizations. 17 spots remaining.

getdolly.ai if you're curious. Happy to talk architecture, use cases, or what we got wrong in v1.

I made a app that does height, length and weight in Bananas of any item you'd like to search can be by picture or by text so if you ever wonder how many bananas big something is give it a try!

howmanybananas

Hey guys,

​I wanted a way to track my coding stats and show them off on my GitHub README, but I didn't want to deal with WakaTime's bloated CLI or pay a subscription just to see my historical data.

​So I made an open-source alternative called TakaTime. The goal was simple: keep it extremely lightweight, make it 100% free, and give you total control to customize how your stats look.

​The setup:

Instead of a heavy Python script, it uses a lightweight Go daemon and editor plugins (Neovim/VS Code) to track your time with zero lag. It saves to your own database (MongoDB/SQLite), which means you have infinite free history. No paywalls, no lock-in—total free will over your data.

​The fun part:

I just added a web generator so you can visually customize your stats card. You can pick from 15+ themes (Tokyo Night, Catppuccin, etc.), tweak the hex colors, and instantly copy the markdown to flex your stats on your GitHub profile. There's also an offline terminal dashboard built in if you just want to look at your stats locally.

​Let me know what you think or if you want to see any other themes added!

​Repo (star us on): https://github.com/Rtarun3606k/TakaTime

​Web Generator: https://rtarun3606k.github.io/TakaTime/

Wiki : https://github.com/Rtarun3606k/TakaTime/wiki

.

https://preview.redd.it/ltei2e8q6ozg1.png?width=2534&format=png&auto=webp&s=61543356c53396ddd8e6e8e1b16aadb46d6ed713

Genuine question. I'm a Max user ($100) and using it since Aug 25 and for the past 2 weeks I'm starting to feel "uneasy" with my Claude Code. (as many others in previous post here)

It started just with faster limits, more token use (really dislike the "auto mode" on how noisy it is), then today out of curiosity looked at the /features listed and actually surprised how many of them there (not to mention my on custom ones as well).

So I wonder about how folks look at this here? Do folks use all or most of these on a daily basis? Are these all needed and I'm just a noob who's doing it wrong? Or is CC going the "Windows bloat" way? (for context was always a windows user and 6 months ago fully went to Linux - and so this "feeling" maybe is just my projection here)

Curios to here others opinions here.

Hey everyone,

I recently built an open-source project called ChitraDEV.

The idea came from a designer friend of mine. He wanted a simple way to generate a lot of visual ideas using AI before committing to one direction and building on top of it.

Not necessarily final polished images.

More like:

• 20 rough visual directions
• 50 moodboard-style ideas
• different concept variations
• quick creative exploration before choosing one path

The problem was that most AI image tools either have usage limits, credits, subscriptions, or hosted workflows that make bulk experimentation annoying.

So I built ChitraDEV as a local/open-source way to help designers, creators, developers, and other visual thinkers generate ideas more freely.

The goal is simple:

Generate many ideas locally, choose the best one, then build on top of it.

I’m sharing it here because I’d love feedback from people who work with AI images, design workflows, local models, open-source tools, or creative automation.

What I’d especially love feedback on:

• Is this useful for your workflow?
• What features would make it genuinely better?
• What local image generation pain points should I solve next?
• Would batch ideation, prompt presets, or image comparison boards be useful?

Project: chitradev.yuvrajraina.com
git: https://github.com/yuvrajraina/ChitraDev

This is part of my giving-back-to-the-community drive, so I’m open-sourcing it and would love to hear what the community thinks.

Have any of you attempted to get an LLM to play Sokoban? I’m curious—given the current state of LLMs, can they actually complete a level without using external tools?

I provided a model with the level below. It seems like a dead-simple logic puzzle, but for some reason, the model consistently fails to grasp it.

####### #P # # # # # # B B # #. . # # # ####### 

Legend:

• "P": The player-controlled character.
• "B": The boxes.
• ". ": The target goals for the boxes.

Why do you think such a powerful reasoning engine struggles with such basic spatial logic? Is it a "tokenization" issue or a lack of an internal world model?

When you create a custom MCP and add it to Claude Code, how do you signal to Claude Code that your MCP "needs authentication"? For example, Anthropic has things under claude.ai like

claude.ai ❯ claude.ai Atlassian · ✔ connected · 37 tools claude.ai Figma · ✘ failed claude.ai Gmail · △ needs authentication claude.ai Google Calendar · △ needs authentication claude.ai Google Drive · ✔ connected · 8 tools 

and if you navigate to the 'needs authentication', it has an option for choosing to authenticate:

Claude.ai Gmail MCP Server Status: △ needs authentication URL: https://gmailmcp.googleapis.com/mcp/v1 Config location: claude.ai ❯ 1. Authenticate 2. Disable 

but how does that happen? Or is this baked into Claude for their specific MCPs?

I built a small tool to route trading alerts via webhooks (Telegram + Discord)

I used to constantly juggle between different bots, webhook URLs, and alert formats while running trading strategies. It got messy fast.

I looked for existing solutions, but most of them felt a bit too heavy for my use case — either required more setup than I needed, or came with pricing that didn’t really fit a simple notification workflow.

So I built a simple tool that lets you:

- Start instantly (no login, no credit card required — free plan)

- Connect TradingView → Telegram / Discord in minutes

- Real-time webhook alerts without VPS setup or extra infrastructure

I originally built this just for myself while working on trading automation, but it turned into a small side project.

No fancy pitch here — just sharing in case someone else is dealing with the same mess.

It’s still early and definitely has rough edges — would really appreciate any feedback.

If anyone’s interested, I can share the link in the comments.

So I built this platform where every recommendation is based on real people, no AI or view-driven algorithm. Point is to make people enjoy content once again.

Youtube's algorithm has ruined it. It only pushes brainrot and fast paced videos, or the same 10 creators.

So we need your help to curate this feed, which is manually checked and approved. So no trash here.

https://preview.redd.it/ogxkcuwh5ozg1.png?width=1222&format=png&auto=webp&s=04f6b3d90a7b921ab25802e535c1abef95ab0580

This is so frustrated, I feed Claude Code full data for it to analyse, and it only read just a bit and gave garbage analysis because it thought that it is enough. I don't know what the hell is going on with Anthropic, surely they are geniuses who can give accurate work with just 15% of 100% data, all the rest can be fabricated.

Hello everyone!

I made a tetris like block puzzle game for android. You play it by placing blocks from the piece tray on the board and then you get new ones to place on the board and you keep on going.

The main difference between my game and others is the theme and the three powerups. First is the remove powerup. When you tap the remove powerup you can tap any block on the board to remove it. Second is the rotate powerup(my personal favorite). When you tap the rotate powerup you can tap any piece(shape) in the piece tray to rotate it until it can fit on the board the way you want it too. Third is the dice powerup. When you tap the dice powerup the pieces that are currently on your piece tray get shuffled and you get new pieces. So if you have all three pieces on the piece tray all three gets shuffled, two or one piece(s) only those get shuffled and you'll still have two or one.

I made this with Java/XML with Appodeal for ad monetization.

Main things I'm looking for feedback on: Does the Play Store listing make you want to download it?

Does the dark neon visual style work or should I add a light themed version ?

Would the powerup mechanics keep you playing?

How smooth are the features of the game ? Etc, do the blocks move to the piece tray smoothly ? Are the special effects after a line clear smooth ? Do the sounds match the clicks ?

If you have any other suggestions or issues please leave a review on the play store or comment under this thread.

https://play.google.com/store/apps/details?id=com.indiedevstudios.blockpop

Thanks everyone.

Every time I wanted to give my agent a new capability (scrape Twitter, pull Reddit posts, find leads, check on-chain data), I ended up signing up for yet another service, getting an API key, learning their schema, and topping up another balance. I was spending more time wiring up tools than actually building.

So I built Monid. It's OpenRouter for agent tools. One API, one balance, hundreds of tools for your agent.

The video shows Claude Code using it to find viral memes across TikTok, Instagram, and Reddit at the same time. It discovers what tools exist, picks the right ones, runs them in parallel, and shows you the total cost at the end. You don't manage any of that yourself.

Beyond memes, agents use it to read Twitter, Reddit, LinkedIn, and Amazon, pull sales leads, monitor competitors, source ecommerce products, check blockchain data, and a bunch of other stuff I keep finding new uses for.

Setup is one line you paste into your agent. That's it.

Free to try, pay-per-use after that (you only pay the underlying tool's price, transparently): monid.ai

Would genuinely love feedback, especially on what tools you wish were in there. If you tell me what you're trying to build I'll see if there's a tool that fits.

Hey, built an MCP server for AI agent audit trails. Got indexed on Glama,

picked up 700+ downloads organically. No issues filed, no questions, no feedback of any kind. I told myself users were just quiet.

Yesterday I tested it on Windows for the first time. Three silent failures:

1. `execSync` with shell + `python3` — doesn't exist on Windows,

   crashes before any useful error reaches the MCP client

2. `postinstall` script using `true` and `2>/dev/null`,Linux shell

   builtins, fatal on npm install on Windows

3. No ImportError handling in the Python bridge, piqrypt not found produced a raw stacktrace that stdio transport swallowed completely

Fixed yesterday. Switched to `spawnSync` without shell (args passed directly, no quoting issues), removed postinstall, added structured JSON error on stderr when the Python dependency is missing. Full chain

validated end-to-end on Windows.

The irony: silent failures are the worst possible outcome for a tool whose entire purpose is making AI agent behavior auditable.

If you ship MCP servers, test the stdio transport on every platform. The client swallows a lot.

github.com/PiQrypt/piqrypt-mcp-server

"why do you get girls and i get airplanes?"

Shipped this morning. Bit nervous.

I'm a web dev, two kids, and my wife carries pretty much all the mental load in our house. The school stuff, the clubs, the nursery, the birthday parties, who needs what kit on which day. It lives in her head because it all lands in her inbox. I'd help when she asked but she was the one holding the whole thing together, and you could see it wearing her down.

What got me was realising it isn't really a "communicate better" problem. The school emails her. The reminders ping her phone. I literally can't see what's coming unless she tells me — and her telling me is more work for her. The setup is broken before either of us has done anything wrong.

So I built Quiet Loon. You forward a school newsletter or club email or whatever, and it pulls out every date, deadline and task automatically. Events go into a shared family calendar (works with Apple, Google or Outlook). Tasks go into a shared list. Morning of any event, everyone in the family gets a WhatsApp. Other parent doesn't need to install anything or sign up — just needs WhatsApp.

Forward once, stop thinking about it. That's it really.

Stack if anyone cares: Next.js, Sanity, Vercel, Postmark for inbound email, Convex, Twilio for the WhatsApp side.

It's at quietloon.com. 14-day trial, £4.99/mo basic, £9.99 if you want the WhatsApp reminders and the Sunday digest.

Happy to hear what's broken or weird about it. The thing I keep going round on: there are a couple of UK competitors clearly building toward similar products (one on a waitlist, one in beta) — is being first-to-shipped-and-paid actually worth anything, or do they overtake the moment they launch? Genuinely don't know.

started this because my nephew kept asking me to help him find bedrock addons on his tablet. every site he used was either full of ads, broken downloads, or just sketchy. figured i could do better in a weekend. that was about eight months ago.

the weekend turned into nights and weekends for months. biggest lesson was that niche doesnt mean small. bedrock players are a huge group and most of them are younger kids who need something clean and simple. that changed how i designed everything.

real numbers: took about 3 months to hit 1k downloads. then it kind of jumped around 5k after a few youtube creators mentioned it without me asking. now sitting around 12k installs. not life changing money but way more people than i expected for my first real android project.

hardest part was content curation. keeping mod listings updated manually is brutal. still figuring out how to scale that part.

anyone else built something for a younger audience? curious how you handled the ux tradeoffs. link in the comments if anyone wants to try it

Lately, instead of ssh-ing into my dev box and the run claude cli from there, I've been using ssh through claude's windows client. This has resulted in lots of sessions abruptly ending when the window has gone black and unresponsive. This does not seem to happen when working on local repos.

Anyone know a fix to this, or if it's a confirmed bug?

There are many reddit summarizer on the internet, and it doesn't take much of an effort to build another one, that talks how I want it to talk.

I pointed claude with screenshots of existing summarizers on the internet/chrome store, as well as the new 4GB model that is shipped with chrome called Gemini nano. So this whole thing can be done without sending anything to internet.

After a few iterations and 59% of the session usage, I am at some good starting point.

https://preview.redd.it/hlmfnxh9eozg1.png?width=1600&format=png&auto=webp&s=18782c5079510fcd6d4c4b292c6623b3c3440510

Should I just release it as is, or keep adding models like BYOK with various api endpoints? Though I'm sure any of you can vibe code a similar tool very quickly.

(PS: I'm still experimenting with it, so did not release the code yet, planning to do after it gets some shape.)

302 users, 48 returning users, 3 minutes engagement time in average, 6.4 pages per session in average.

• Modern website for searching mountain huts in the Alps.
• Target audience: Germans and Internationals living in Germany.
• Got published in the local press last month.
• 7 of 8 cold mails to regional hut tour guiders are unresponded since a week.
• Friends with hiking background like the project and use themselves.
• SEO: Published 2 articles.

Solo founder, 26 years old. I am worried that the performance is low, it's been almost half a year.

How's the website different from others:

• Allows search huts by dates like AirBnb
• Give clear information for transport accessibility
• Provides links to useful web resources: digital maps, official websites, wikipedia, local hut portals.

Currently, I have an assumption that since average age in Germany is quite high (46), the website design might look way too modern.

Will be happy to provide more insights, to receive any comments including how to improve the return rate and acquire more users.

"Make something that looks nsfw but isnt"

So guys I’m absolute beginner in ComfyUI. Started two days ago with ChatGPT help. Now he told me that if I need my girl to be same every time i need to train LoRA. Ok but also he told me to find 20-30 photos of the same girl (AI girl) and then we can start training. My question is where i can find 30 photos of nice AI girl. Is there Face packs or something like that?

Hi friends.

I'm looking for 2 models, one for realism and another for anime, but with few steps. Since 30 steps on my PC is around 12 minutes.

"z-image turbo aio (all-in-one)" has a version for realism and another for anime, it is the one I am currently using, but unfortunately I find it very limited in terms of no-sfw.

I was wondering if maybe there is some derivative/evolution of "sdxl" or "z-image" for a few steps, and that is not limited in terms of no-sfw.

Regarding anime, I have tried "Anima", but it is 30 steps, very slow for my PC, and also, for some reason, the images look very flat and basic, unlike "z-image turbo aio (all-in-one)". But maybe this is my fault for not using prompts and natural language correctly.

And regarding realism, there are more base models and derived models, so I am not very informed.

Thanks in advance.

Hi everyone, I (F19) am a summer intern working closely with a director at a consulting firm in India, and we’re currently trying to find an AI tool/workflow that can help us monitor and synthesize business developments in real time.

The key focus areas are:
- DEI
- Workplace culture
- EVP / employer branding
- M&A activity
- Sales force effectiveness

Mainly across Financial Services and General/Industrial sectors.

What we’re looking for is not just a news summarizer. We want something that can:
- track daily/weekly developments
- identify weak signals and emerging patterns
- connect developments across companies/sectors
- surface trends before they become obvious
- potentially hint at upcoming M&A, restructuring, talent shifts, culture problems, etc.

The ultimate goal is to use these insights proactively while pitching to new and existing clients — ideally before competitors, and sometimes even before the client fully realizes the issue internally.

Would appreciate recommendations on:
- AI tools/platforms
- custom workflows
- agentic setups
- newsletter intelligence stacks
- OSINT approaches
- integrations (Slack, Notion, Teams, CRM, etc.)
- how consulting firms / strategy teams are approaching this internally

Open to both enterprise and scrappy solutions.

I recently got a lot of feedback for my waitlist website, launchhq.space asking for Google sign in. This made me realize that manually asking people for their email and a password was a friction point that stopped a lot of people. I had to think in the shoes of my customers, just because I do not use Google sign in doesn't mean that others don't. So with that being said, if anyone else has more useful feedback for my site, it would by greatly appreciated.

I’ve been annoyed by this for a while.

Every time someone posts “we hit 94.2% on benchmark X”, there’s usually no way to know whether that claim was locked before the test run, or shaped after seeing the results.

Pre-registration mostly solves this problem in social science, but ML never really adopted an equivalent habit. My guess is that the format was never small enough or practical enough for people to actually use.

So I spent the last few weeks building a tiny spec for it:

PRML Pre-Registered ML Manifest

The idea is simple: before you run on the test set, you write down what you are claiming, serialize it in a canonical form, hash it with SHA-256, and publish the hash publicly. Later, anyone can check whether the manifest matches the hash.

So the eval claim is locked before the result is known.

Spec:
https://spec.falsify.dev/v0.1

Repo:
https://github.com/studio-11-co/falsify

The manifest has 8 fields:

claim
dataset_id
split_hash
metric
threshold
model_ref
timestamp
author

There are four reference implementations: Python, JavaScript, Go, and Rust.

All four produce byte-identical SHA-256 hashes across 12 conformance vectors. That part took longer than the spec itself. YAML libraries disagree about more things than I expected.

A few things I changed along the way:

First draft used JSON. I switched to YAML because researchers actually read manifests, and JSON is ugly for comments and multiline context.

The first version had 12 fields. I cut it to 8 because anything longer felt like nobody would fill it in.

I almost shipped without strict canonicalization rules, then caught JS and Rust disagreeing on float serialization.

What is still missing:

No registry yet. Right now you self-host the manifest.

No GitHub Action yet.

No default third-party timestamping flow.

v0.1.3 is still a working draft, and I’m fine breaking things in v0.2 if the design is wrong.

The feedback I’m looking for:

If you published an ML benchmark in the last year, would you have used this?

What would have stopped you?

Are the 8 fields enough?

Is anything missing?

Is anything dead weight?

And is “lock the eval claim before you see the test set” the right framing, or is there a sharper way to explain it?

Solo side project. Spec is CC BY 4.0, code is MIT.

Please roast it.

Me and my girlfriend split bills but it's never a clean 50/50. Mortgage is split, council tax is split, but my car finance is just mine, her subscriptions are just hers. Every app I tried just lumped everything together and gave me a household total which is completely useless.

Ended up just building something myself. Been using it for a few months and it's actually sorted my finances out properly for the first time.

The main thing it does is let you set your share on each transaction — 100%, 75%, 50%, 25% — so every total you see is your actual money, not the household pot. You can star your essential bills to separate them from general spending and set a monthly budget for everything else so you know exactly what you've got left.

Other bits:

• Lloyds CSV import, takes about 2 mins a month
• Savings goals with a projection slider up to 20 years, compound interest built in
• Take-home pay calculator with this year's tax rates, salary sacrifice and all student loan plans
• 5 colour themes because I wanted it to look good and actually open it

It's a PWA, add it to your home screen, no app store.

Hey folks,

Solo founder from Germany here. Currently running three live products (B2B SEO reporting tool, multi-tenant wedding website platform, local SEO tool for trades) while holding a comms job in the public sector. So I'm writing this fully aware that I should not be starting a fourth thing right now.

But two ideas keep nagging me, and I want to gut-check which one has the stronger long-term play before I park or kill one.

Idea 1: Name change co-pilot (post-marriage)

When you get married in Germany and take a new name, you have to update ~40 different entities: ID card, passport, driver's license, tax office, banks, insurances, employer, utilities, social media, and so on. The current state of the art is a static PDF checklist from 2021, hosted by an insurance lead-gen site.

The product would be: 90-second quiz → personalized checklist based on life situation (car owner, kids, self-employed, property owner) → location-specific government office addresses with online booking links → pre-filled letter templates → email reminders over 6 months.

• TAM: ~520k name change events/year in DACH (marriages + divorces + naturalizations + birth registration)
• Monetization: 19.90€ one-time premium + affiliate stack (insurance comparison, bank account switch, address change service – 25–80€ per lead)
• Realistic year-2 target: 17–28k€/month combined
• I'd build it from my existing stack (Supabase, Vercel, Stripe, Brevo, PDFShift, Claude API) – setup cost ~440€, ~80h dev time

Idea 2: Home network planner

For non-tech consumers who want a stable home network. Floor plan input → recommended hardware (router, mesh nodes, switches) → cabling guide → live price comparison across retailers → step-by-step setup. Phase 2: smart home compatibility layer (Matter, Zigbee, HomeKit cross-compatibility checks).

• TAM: harder to quantify but huge. Every German household with WiFi pain. Adjacent: smart home market growing fast.
• Monetization: affiliate revenue on hardware (3–8% on 200–600€ baskets) + premium config service + potential white-label for ISPs
• Realistic year-2 target: unclear, but lower price-per-unit and higher transaction volume than the name-change tool
• Same stack, similar build effort

Honest comparison:

What I'd love to know:

1. Which one would you bet on for a solo founder bootstrapping nights/weekends?
2. Is the once-per-life event a feature (clear scope, easy to finish) or a bug (no recurring revenue, no retention)?
3. Anyone tried affiliate-driven B2C? Real conversion rates would help my model.
4. Am I overweighting the wedding-platform synergy? It feels like a moat but might be a distraction.

Not looking for "just build both" or "do customer interviews" answers – I know. Looking for the gut take of people who've shipped consumer SaaS in regulated/info-dense niches.

Will share results in a follow-up post once I decide. Thanks for reading.

ship a feature in an afternoon. claude code does most of it. you review, you test, you commit. feels great.

three days later something breaks. you open the file. you have no idea what this code does. you wrote it. or you watched it get written. same thing now.

so you ask claude. claude explains it. you nod. you forget by tomorrow.

the worst part isn't that it's hard. it's that there's no resistance. nothing pushes back. you don't earn the understanding. you rent it for the 20 minutes it takes to fix the bug and then it evaporates.

i shipped 3 features this week. i could not tell you how 2 of them actually work without re-reading them. that didn't happen before.

the building part got faster. the owning part got harder. nobody tells you that. all the demos show the green diff. nobody shows the file you're going to open in 6 weeks where past you and past claude conspired to do something clever and now neither of you is around to explain it.

every variable past me named data. every helper named process. why. who hurt you.

i'm not complaining. i'm shipping more. clients are happier. but there's this background hum of i don't actually live in this code anymore. like i moved into a house i didn't build and someone else picked the wallpaper.

does anyone else feel this. does it pass once you adjust your review process. or do you just get quieter about it.

Hey guys. Made Claude skill to quickly deploy your projects without leaving your Claude session.

Not Git needed. Free for 1st project.

Skill: https://github.com/cybrixcc/cybrix-skills

Homepage: cybrix.cc

Please share your thoughts. Star on GitHub if you like it.

I'm going round in circles (not techy!). I need to set up a flow where I have a bank of inspiration images, and a text prompt - overnight I'd love an agent to create me new images based on the inspiration images and text prompt and deliver them to me (via whats app would be great but I dont believe I can do this!!!) or a link where I can 'approve' or 'reject.' I'd love to use Gemini or Mid-journey but I think MJ might be a bit more difficult to set up? I've been using Cowork and it's built the artifact but in reality it just doesn't work. It's asking me to connect MAKE - is this the missing piece before I waste any more time?

How do you track multiple projects when cloud tools are simply not an option?

I've been dealing with this for years, the default answer is always "cobble together Excel files", but it gets messy fast across several projects.

So I put together a program management dashboard that lives entirely in a single HTML file. You open it in your browser, that's it, 100% offline, no install, no account. An Excel file generated by the app is the database. Core features: Gantt, Kanban, Risk register, KPIs per project and a multi-project view for both Gantt and Kanban. Your data never leaves your machine.

Before I go further with it, I'd love honest feedback:

→ Is "Excel as the database" a strength or a dealbreaker for you?
→ Does the single-file approach make this a non-starter in your org, or exactly what you need?
→ What's the one thing missing that would make you actually use this?

Maintaining a comparison site (sweepbase.net) for a year. Last month I extracted the underlying card list and published it as a GitHub awesome-list — 136 crypto debit/credit cards, grouped by region, custody, cashback model, and card network.

https://github.com/mbtrilla/awesome-crypto-cards

Why publish it separately from the site? Three reasons: 1. The site has affiliate links on review pages. The GitHub list does not — it's enforced by awesome-lint in CI. 2. Devs and researchers want a flat dataset they can curl or fork. Not a JS-rendered table. 3. If someone wants to fix data, a PR is way less friction than emailing me.

One thing I didn't expect: filtering across 8 regions and 5 custody models in markdown is annoying. Each new card means editing 4-5 list sections to keep counts accurate.

Star or fork if useful. PRs welcome — especially for cards I missed.

I wanted to try installing a simple small model like nemotron-3-nano:4b from ollama and try it for simple quick fixes offline without burning credits or time.

the model works well on ollama run time but when I try to use it on opencode, the device heats up but there is no output and just keeps running like that for a while until I decide to exit opencode.

the model fits perfectly on my hardware: 4gb Vram cc 5.0, 16gb ram, core i7 7th gen hq.

also it is tagged "tools" on ollama's web page so it should be okay for tool usage + they provide the command to launch it on opencode.

what am I doing wrong?

ChatGPT is a browser tab. Hermes is a server process that's running right now, has been building a model of your workflow for the past few weeks, and just sent you a Telegram message before you woke up.

Five actual differences worth knowing:

1.It doesn't stop when you close your laptop. The hermes AI agent lives on a server, not inside a browser session. It runs continuously whether you're looking at it or not.

2.It messages you first. Hermes sends a Telegram briefing at whatever time you set, no input required, it just goes, no chatbot in the world does that.

3.Memory that gets useful over time. By week four or five, hermes knows which tasks you always reject, what format you prefer for email drafts, which contacts you respond to fast and that context compounds.

4.Runs code and commands, doesn't just write them, commits go to GitHub, files move, scripts trigger. Not "here's the code" but the thing is done.

5.Takes action in your actual apps. Email gets sent, calendar event gets created, form gets filled, the gap between "here's a draft" and "I sent it" is the entire value.

I’m a minimalist at heart, but naming my project pushed me to the edge. I eventually landed on Koan, but it wasn't an easy choice.

To be honest, I’m tired of the "mystical Zen" aesthetic that’s taken over the mindfulness space. Usually, when you see a "Zen" app, it’s wrapped in layers of complex, hyper-religious, or "mysterious" narratives. It feels like a ceremony I didn't sign up for.

I wanted to do the exact opposite.

To me, a Koan isn’t some esoteric riddle that requires a master or a monk to solve. It’s just a tool—a mental crowbar to break the loop of your logical brain. It’s that "hard question" that forces you to stop overthinking and actually start feeling.

I built this app because I realized traditional journaling often fails because it’s too noisy. We’re either writing boring logs about what we ate, or we’re getting lost in the "ritual" of the app itself.

So I stripped everything away. No mystical baggage, no complex rituals, no "Dear Diary" fluff. Just a single, sharp prompt and a space to answer. I wanted to create a direct, sometimes uncomfortable, but always honest encounter with yourself.

It’s been a weird journey building a tool that fights against the very category it’s in (the "mindfulness industry"), but I’m curious if this resonates with anyone else here.

I just launched on the App Store and would love some brutal feedback on the UX or the concept itself. Does "Koan" feel too heavy for a minimalist tool, or does the "anti-mystical" approach make sense?

GLM is the most schizophrenic model so far on plan-mode. It both under-clarifies on clear ambiguity (4 audit prompts) AND over-clarifies on degenerate inputs (whitespace, single char) AND over-clarifies on multi-turn answers (reclarify_partial_answer — the user provided answers, model asked again). Both directions failing in the same run suggests GLM doesn't have a stable internal sense of "is this ambiguous or not" — it just has a "should I ask?" coin flip with the bias varying by input shape.

Anyways here are some benchmark on tool calling

Chat ate.

I'm trying to use Claude's voice mode. I've got it working in my browser and on my android app, but there's no voice mode button (just the microphone button) in the desktop app. What settings do I need to change to get voice mode to work in the desktop (windows) app?

We all been here. Token limit hits, deadline doesn't care. Thought this needed to exist in physical form.

Would you wear this? hahaha

Title

I was in this sub because wanted to learn how people use AI. Yet now just seeing the gore images nonstop. Its ok for me if people share it between each other but not everyone uses +18 filter and I dont want to look at the same bloody nonsense. And still interested in how people use the images but this is repetitive and disgusting.

I think this should be moderated.

I'm running models via llama.cpp (webui with MCP, and Opencode for agentic coding).

In some model tools just work (using mcp in the webui, creating files in Opencode etc), in other not at all. When they are not working I either get literal tool call in the chat, like
[tool\_call: write\_file for path
or simple refusal "I cannot use tools.."

Why is that? Is that the model itself (to old), or some proplematic chat template in the gguf, or the ggug itself broken? Or to I need specific arguments to run llama.cpp (doing the standard --jinja right now)?

If anybody could shed dome light on this I would be grateful. Have a hard time figuring this out.

Model Examples:

Working:
gemma-4-26B-A4B-it-heretic.Q4_K_M
Qwen3.6-35B-A3B-UD-Q4_K_M

Not working:
DeepSeek-Coder-V2-Lite-Instruct-Q4_K_M
qwen3-coder-30b-a3b-instruct-awq-q4_k_m

Thx!

been running 5 production agents and got hit with a $4k API bill in a single month early on. dug in. cut spend by about 70% over 6 weeks. the patterns that mattered: cheap model first, expensive on retry. claude haiku handles ~95% of tasks, retry with sonnet only on validation failure. cuts spend significantly with no real quality drop. aggressive context window pruning. early agents were sending entire conversation history every call. switched to relevant exchanges + a state object. cut input tokens 60%. prompt caching for repeated system prompts. 30% drop on agents that send many requests. structured output beats free form for short tasks. saved another 20%. monitor cost per workflow not just per agent. one specific endpoint was returning malformed JSON. claude kept re parsing. blew up token usage 5x. wouldnt have caught it without per workflow tracking. the meta pattern: agent costs are nonlinear. you need observability into cost per tool call, not just per agent run. anyone else have cost patterns that arent obvious?

Qwen/Qwen3.6-35B-A3B was released 22 days ago

Qwen/Qwen3.6-27B was released 15 days ago

Let's predict when we can expect the 9B and 122B versions

Created Instagram carousel template in Claude Design.

Now I want to make a Claude Project that automatically generates slides using that template, based on the text I input.

How do I do that?

I exported the template as a standalone HTML, tried uploading it to Project Knowledge Base but it just stays stuck indexing it. What am I missing?

I'm new to all this, so excuse me if I'm asking a really dumb question.

Thanks for all the help 😄

Tech Stack Required for a Solo Startup in 2026:
- Codex / Claude Code for logistics
- coremate's OpenGUI for distribution
- Stripe for payments
- Posthog for analytics
- Kit / Beehiiv for email subscriptions
- Vercel for hosting and deployment - Supabase for database, backend, and authentication

I built a tool called swokei that analyzes business websites for issues in design, SEO, and mobile optimization. It then generates personalized outreach messages that are ready to send directly inside the platform. You can also run full email automation campaigns within the platform.

dicking around with the new mtp speculative decode with qwen3.6 27b, and it’s great. but for agentic coding i’ve seen significant improvements from ngram, because a decent fraction of the time (e.g. calling edit tool) the model is just repeating verbatim a section of code that it has already seen before. ngram can speculate on a lot of tokens reeaallly fast in comparison.

it’d be great if we could combine them by using them both at the same time, but it looks like if i add them both to the command line arguments, only ngram is active.

is there any reason both can’t be used simultaneously? fundamental limitation, or just an implementation limit with a fix on the horizon?

EDIT: just looked at the PR again and PmNz8 asked the same question like two hours before i posted this. go give it an updoot! https://github.com/ggml-org/llama.cpp/pull/22673#issuecomment-4394544777

We’re a tiny marketing operation, two people. I handle the technical side — skills, MCPs, pipeline plumbing.

My colleague does the actual campaign work day to day. She’s fluent in ChatGPT and Claude chat but has never touched a terminal in her life.

We’re moving most of our campaign production into Claude Code because the multi-step stuff we run just doesn’t fit the chat interface anymore. That part I’m not worried about.

What I’m stuck on is the boring question nobody seems to talk about: how do you actually work as a team when both people sit in their own local Claude Code session?

She drafts a briefing in her session. I want to review it before it goes out. How does that flow without me jumping onto her machine or both of us losing context? When we iterate on the same document, who’s holding the pen?

Approval gates matter because there’s real ad spend on the line and we can’t just yolo it. And then the “where are we”-state — Monday morning, who knows what happened Friday afternoon.

My current direction is a Git repo for skills and templates (auto-pulled so she never has to think about it), Supabase as the source of truth for structured data via MCP, Drive for the human-viewable stuff like PDFs and assets.

Plus probably some kind of handoff mechanism that writes a state summary somewhere we both see.

It works on paper but feels like I’m reinventing something that small teams must have figured out by now.

Anyone running Claude Code with a non-technical teammate for marketing, content, agency or consulting work?

How do you handle review loops, approvals, and just knowing what the other person is up to without a Slack thread per task?

Curious what stuck for you and what broke

Good morning, I needed a tool that would allow me to have multiple AIs within it, so I downloaded Google Antigravity, but having only the provided models wasn't enough since it's a fork of VS Code, I decided to swap out the store links with the base VS Code ones and install various tools for programming and testing, including the Claude Code extension (with a Pro subscription).

After 2 or 3 days I noticed it doesn't save chats. Everything else works great has anyone had a similar experience? Do you know if there's something extra I need to do?

I'm stuck either keeping Antigravity open and never closing it or I'll lose the conversation, or each time generating a .md file of everything done up to that point and having it analyse that in the new chat — or if I wasn't able to create the .md file, I have to make it re-analyse the entire project from scratch, which is a huge waste of tokens.

I use Windows 11 OS

video sourced from x

I'm having some trouble here with trying to understand how to use local LLMs with existing code bases. I understand the flow of trying to create something new or trying to do document analysis but in terms of existing code, how do I point it towards my code base, ask it to go through it completely, and utilize those kinds of features?

I did try using Claude Code via the desktop cmd but it takes way too long as compared to just using ulama from CMD so if you have any other suggestions, I'm open to it.

Currently using qwen3.6:27b on this rig:

Processor AMD Ryzen 9 5950X 16-Core Processor (3.40 GHz)
Installed RAM 64.0 GB
Graphics card NVIDIA GeForce RTX 3090 Ti (24 GB)

I’ve just started messing around doing images and shit with ChatGPT, but I don’t know how to “prompt”.

So, as the title says.

Please and thank you!

I am saving at-least $100-$200/month on AI subscriptions because of this one simple realization:

Your AI is only as good as you.

I’ve had a Claude Pro subscription for a while and honestly, I love it. But the usage limits are brutal and we all know that. Every 4th day of limit reset I’d hit “Usage Limit Reached” right in the middle of building something.

For context, I use AI heavily:
• Vibe coding
• Building agents
• Automating random workflows
• Creating docs/tools
• Brainstorming ideas
• Testing MVPs

This week I was building LinkedIn AI agents and Claude hit its limit again. I was frustrated because I was so close to finishing it.

Then I remembered I have an old Gemini Pro subscription from a promotional offer they ran last year. Never touched it seriously before (except antigravity but stopped using it later when they introduced heavy limits) because I assumed Gemini still wasn’t at the “agentic” level of Claude Code/Codex and the most important, I ignored Gemini CLI completely.

The last few days, after Claude hit its limits, I started using Gemini CLI instead.

And It picked up right where Claude left off! Like WTF!

I completed the setup and also added extra features and I only used around 7% of the quota.

That’s when it clicked for me:

I am not limited by the model. No one is. It’s just sometimes, we get too comfortable with one “system” and feel stuck when it’s taken away. You can have access to the best model on the planet but someone with a proper understanding of what they want, would end up building a better product even with a “not-so-world-class” model.

Now my setup looks something like this:
• Claude → planning, architecture, deeper reasoning
• Gemini CLI → execution, expansion, iteration, shipping

Instead of paying for more limits on one tool, I opened up an entirely new lane by learning how to orchestrate them together.

Feels like discovering a second brain you already had access to.

For about a month my workflow has been the same broken loop: 1. I'm in Excel/Outlook/SSMS/whatever, working on something. 2. I hit a wall — wrong SQL syntax, an Excel formula I forgot, a customer email I need to summarize. 3. Screenshot the relevant area. 4. Alt+Tab to ChatGPT. 5. Paste the screenshot, write a paragraph explaining context ("the sheet is about.. and formula needs to…"). 6. Wait for the answer. 7. Copy it back, paste it into the right place. 8. Repeat. 30+ times a day. Every day. The AI response itself is usually fast. The friction is everything around it. After enough months of this I realized the missing piece isn't a smarter chatbot — it's an AI that already sees what I'm looking at, so I don't have to explain. So I started building one on nights and weekends. It's called Mudrik (Arabic for "aware"). Press Alt+Space anywhere in Windows and a small panel slides in, already aware of the active window — every visible button, field, label, value. You ask in the FEWEST possible words ("fix this", "draft a polite reply", "what does this toggle do?") and it answers, drafts, or even types/pastes/clicks for you. A few honest caveats: - It's in public preview. Rough edges exist. - It reads UI structure through Windows accessibility (the same layer screen readers use), not screenshots — so it's privacy-friendlier than vision-based tools. - Bring your own LLM — Claude, GPT, Gemini, local Ollama, etc. Just paste a key in settings, or use the free tier on OpenCode. - Sandboxed: no shell access, no filesystem writes, no network calls beyond the LLM you choose. - Windows only for now. The thing I didn't expect is that I now use AI for the small questions I previously skipped — because the friction is gone. That alone has changed how I work more than any model upgrade. Repo with installer and details: github.com/abdallahmagdy15/mudrik Hones feedback welcome — especially "this would be useful if it did X." Already a list of features I want to add but always interested in what people would actually use. 

A few things which I find interesting:

- The system prompt is editable. Drop a `system . md` in `~/.pi/agent` and you fully replace Pi's system prompt. I didn't find this in any other coding agents.

- Sessions are trees, not lines. `/tree` lets you fork from any earlier message. When the agent goes the wrong direction 10 messages ago, you don't restart you /fork.

- Its very minimal only four tools: read, write, edit, bash. No grep tool, no find tool, no git tool. Bash covers it. Mario's argument is that models are already RL-trained on bash, so dedicated tools are added noise.

- No sub-agents built in. This was the part I wrestled with most because my Claude Code workflow leans heavily on `.claude/agents/`, but had fun when I used pi only to create extension for my workflow.

- The agent can write its own extensions. I asked it to build a status bar widget showing my git branch + uncommitted count. It read its own extension docs, wrote the TypeScript, and hot-reload done. Genuinely impressive.

If you want something that works on day one, you can use other coding agents as they are polished products. If you are a minimalist or want to actually own your context and workflow, Pi is ideal for you.

The thing keeping me from switching fully is Anthropic's recent policy means logging into Pi with a Claude Pro account doesn't draw from your subscription's included usage , it bills as extra per-token usage on top.

If you're on a ChatGPT subscription, Copilot, OpenRouter, or running Ollama locally it is too good not to try. Curious if anyone here has been running Pi would love to hear experience.

If anyone wants to see or read my full exploration I have added links for text and video version in comments

Hello,I ve upgraded my pc

rtx 5070

32gb of ram

ryzen 7 7800x3d

I want to try running local ai for game dev,for fun,and right now I have a sata 3 ssd
Is it worth it to get the m.2 nvme 4.0 for 150$?

Or should I just wait for prices to drop?

We keep talking about AI agents like they're just productivity tools on espresso — little digital clerks that book flights, summarize our PDFs, fill out forms, and save us from the thousand tiny humiliations of using software. And okay, sure. That's part of it.

But that's probably the small story.

The bigger story? Agents might become an entirely new distribution layer.

Think about it. If an agent helps someone pick a SaaS tool, book a service, compare vendors, hire a freelancer, buy insurance, or decide which product is actually best — that's not just task completion anymore. That's demand creation. That's recommendation. That's allocation. That's the agent becoming part of the market.

And the moment that happens, the old web monetization machinery starts looking really, really outdated.

Ads. Affiliates. SEO. Attribution. Tracking pixels. Settlement rails. All of that was built for pages and clicks and rankings — visible inventory on a visible web. But agent interactions are different. Intent is way stronger. The interface is conversational. Recommendations happen inside reasoning chains you might never see. Trust is more fragile. Disclosure matters more. And the cost of corrupting that recommendation layer is way, way higher.

So the real question isn't "can agents monetize?" Of course they can. Everything monetizes eventually. This is the internet, not a monastery.

The real question is: what kind of monetization doesn't poison the thing itself?

Do we need a commercial distribution protocol for agents?

Where's the line between a genuinely useful recommendation and a paid placement?

How do developers get paid without turning agents into softly spoken ad networks?

What needs to be disclosed, attributed, logged — or just straight-up prohibited?

And what practices should be treated as radioactive from day one?

Because if we get this wrong, the agent era won't be a cleaner, smarter version of the web.

It'll be the web's worst incentives, compressed into a much more intimate interface.

Would genuinely love to hear from builders, devs, users — anyone who's been staring at this and wondering the same things.

The flow goes like this. made a landing page. hated it. rewrote it.

did that 10 more times over and over

Like different headlines. different angles. different layouts.

none of it felt right and I couldn't figure out why.

then one of my founder friend saw it and said "I still don't know who this is for."

in my opinion that was probably the most useful and most painful sentence anyone has ever said to me about my product.

so I went and talked to other founders. I thought were my target users. just asked them to walk me through the last time they hit the problem I was solving.

Then I rewrote copy again the next day.

signups picked up 16 sign ups in one day, and it was real no fake numbers

I guess the copy problem was never a writing problem at all. it was an audience clarity problem the whole time.

has anyone else gone through this specific kind of copy problem for there app/saas?

8 days ago I launched VisaGuide

Honest update:

✅ 8,000+ visitors

✅ 195+ countries covered

✅ Making first cents on ads

❌ Growth is slow

❌ No budget for marketing

Anyone been through this stage?

How did you push through the slow part?

https://visaguide.cloud/

Curious what tools people here are using for interview scheduling automation.

Not just basic calendar syncing, but AI tools that actually help coordinate candidates, recruiters, and hiring managers without endless back-and-forth emails.

I’ve seen a few teams mention tools like GoodTime, Paradox, and newer platforms adding AI scheduling assistants, but not sure what’s actually working well at scale.

Main things I’m curious about:

handling reschedules automatically

time zone coordination

candidate communication

ATS integration

reducing recruiter admin work

What’s everyone using right now, and what still feels broken?

I’m looking fot options to create a project tracker dashboard on Claude and have it displayed on a large screen. I’d ask Claude but limit doesn’t reset for 4 hours yet in the UK.

Any ideas/tips?

I often see models like Qwen 3.6, DeepSeek V4, MiniMax 2.7, and Kimi K2.6 discussed due to their strong price-to-performance ratio, large context windows, and relatively low API costs.

But I know these are all Chinese models/providers. Interested in comparisons across providers.

I'm doing a blog optimization task to get more traffic from my blogs but Claude is not able to read any of my website links.

Is there any workaround for this?

For me it’s Sonic 3 & Knuckles. I’ve beaten it over 100 times since I did my first playthrough 17 years ago for sure 🤣

Yeah, I wanted that comedic angle, but with a nude model.
3 hours of attempts with Klein 9B and 9B Base gave me no results.

How do you deal with the models.

Klein is a pretty dumb model and refuses to generate the camera angle I need. Here’s how I’m handling it right now:
I generated a one-piece swimsuit for her, then asked banana to generate the angle I wanted, and after that I undressed her again in Klein.

Pretty primitive method, but I couldn’t come up with anything else.
Any ideas? Is QWEN smarter than Klein?

Hey r/sideproject,

I live in Seoul and kept walking past tourists every day thinking they probably want to eat somewhere real, not just whatever shows up on Google Maps.

I'm kind of obsessed with food so I just... started building something on weekends.

Not a developer by trade but got pretty deep into it. Took way longer than expected but here we are.

It's called Sikr (sikr.kr). Free, no sign-up, works offline too.

1,026 local Seoul restaurants across 11 neighborhoods, English menus, and simple Korean phrase cards you can show the owner when ordering. Stuff like "less spicy please" or "one more bowl of rice."

Honestly just a hobby project. If someone visiting Seoul finds it useful that's enough for me.

Would love any feedback on the UI or features. Still figuring out what people actually need.

sikr.kr (free, no account needed)

Listen me out, I know it may sound ridiculous.

I have made those mistakes of crafting the product end to end, making sure every nut and bolt is in place, making sure we are ready for 1M users’ worth of traffic on our launch day. Excited and pumped.

But then reality hits you. You launch it and you receive 100 users max of which almost 80% come from groups you joined, friends, and family.

I have one friend and you should listen to his story. We both decided to do a startup after leaving our company. I followed the path of building things and making sure everything was in place before launch.

But he did something completely different, at that time it was unheard of for me at least. He simply built a landing page and put one CTA: join waiting list. That’s all.

And he spent the majority of his time on Reddit posting and educating people about their product. In just 2 months he had 20K+ users on the waiting list, and the next month he convinced one VC to invest in his idea by showing that waiting list.

To be honest, at that time I felt jealous and stupid, but now I can connect the dots.

I myself am the founder of ogcode -- an open‑source coding agent.

I’m researching the current ecosystem for building production-grade multi-agent AI applications in Python and wanted to understand what developers and companies are actually using in real-world deployments.

There are several frameworks available now such as:

• LangGraph
• Microsoft AutoGen
• CrewAI
• Semantic Kernel
• OpenAI Agents SDK
• Google Agent Development Kit(ADK)
• LlamaIndex

For developers who have actually deployed multi-agent systems to production:

• Which framework are you using today?
• What made you choose it?
• How reliable/scalable has it been in production?
• What are the biggest limitations or pain points?
• Would you choose the same framework again if starting from scratch?

Interested especially in enterprise-grade use cases like:

• AI assistants
• Customer support automation
• Banking/finance workflows
• Research agents
• Tool orchestration
• Human-in-the-loop workflows

Would love to hear real production experiences rather than just benchmark comparisons or tutorials.

You can do a GPU passthrough/partition and get only a 5-10% performance impact from overhead.

Hey folks 👋

Solo dev from Ludhiana, India. Just shipped my first SaaS and would love feedback before I push it harder.

What it is: ListingClimb scans Amazon India listings and scores them on three dimensions:

• Keyword coverage (do you cover what buyers actually search)
• Rufus optimisation (will Amazon's new AI surface you for conversational queries)
• Conversion clarity (bullets, A+ readiness, title structure)

Free scan, no signup needed. Optional AI-powered rewrites for ₹149/scan (~$1.80). No subscription.

Why I built it: Every existing tool (Helium 10, ZonGuru, etc) is a US-priced subscription. Indian sellers don't subscribe to SaaS the same way US ones do, and they want pay-per-use. Plus, none of them leads with Rufus, which is genuinely changing how listings get found on Amazon now.

Stack

• Next.js 14 + TypeScript + Tailwind
• Supabase (auth + DB + RLS)
• Anthropic API (Claude Haiku for analysis, Sonnet for rewrites)
• Razorpay for INR payments
• Apify for Amazon data scraping (CAPTCHA was a nightmare — solved with a "paste listing" mode as primary UX)
• Vercel + Upstash Redis for rate limiting

The hard parts

1. Amazon CAPTCHA blocking server IPs → moved to "paste listing text" as primary flow
2. Claude returning markdown-wrapped JSON 30% of the time → strict schema + retry-with-strip
3. Free scan abuse prevention → device fingerprint + IP record + per-account flag
4. Razorpay verification edge cases → HMAC + server re-fetch + timing-safe compare

What I'd love feedback on

1. Pricing — ₹149/scan, right ballpark or off?
2. The Rufus angle — leading with it, or burying it?
3. Anything obviously broken when you scan a listing

Site: listingclimb.com

Roast away. Honest critique > polite encouragement.

Not going to sugarcoat it — day 1 was decent, day 2 fell off a cliff. Classic PH curve. The real problem I'm realizing is that AnyCompare (anycompare.app) needs to reach people exactly when they're comparing something — not randomly. Working on SEO and finding better channels now. Anyone else been through this? How did you find your first real users post-launch?

Most AI recruiting conversations still focus on sourcing or screening, but honestly the bigger value for our team has been using it as an internal recruiting assistant.

We’ve been using tools like Carv to summarize candidate profiles, prep submissions, draft client updates, and organize interview notes before meetings. It’s less about replacing recruiters and more about cutting down repetitive admin work.

The biggest win is speed + consistency. Recruiters spend more time actually talking to candidates instead of formatting notes or rewriting the same updates.

That said, AI still falls short on nuance, relationship building, reading between the lines, and understanding candidate motivation still need a human touch.

Curious how other teams are using AI beyond sourcing?

I've been building https://www.jrivecontent.com a platform for small startsups and small creators posting for $15-60/video

Just released Rekindle v0.2.0.

This is a local MCP continuity tool for Claude Code. I built it because I was tired of Claude starting cold every session, asking me what we worked on yesterday, and rebuilding context I'd already given it three times. Rekindle gives the assistant a way to actually pick up the thread.

v0.2 adds:

• boot_report orientation at session start
• orientation scoring (so you can see how grounded the assistant is before you start working)
• structured gap detection
• end_session capture at session end
• typed continuity records for checkpoints, decisions, open loops, constraints, warnings, and relational delta

Install:

• npx rekindle init

Release notes: https://github.com/Skitchy/rekindle/releases/tag/v0.2.0

Looking for 5-10 people who run Claude Code on real multi-session projects. Try it across 2-3 sessions and tell me where continuity still breaks.

Hi everyone ,I wanted to share something we’ve been working on. We’re building Airtap, an AI automation product that can operate a phone like a human would, handling small but time sensitive tasks that depend on speed and timing. Our team came up with over a hundred possible use cases, and one example we tested was using AI to try to grab limited edition sneakers. Some people on the team thought it was a fun, attention grabbing example, while others weren’t sure it was the best thing to promote publicly, so I wanted to ask for honest feedback here. If AI could really handle this kind of high speed mobile task for you, would you trust it?

Or would you rather see it handle other tasks first, like emails, spam, ticket booking, reservations, and similar things?

Anyone else spend 10 minutes a month hunting for the same password for the same encrypted PDF from the same bank?

Built an app for exactly this. SmartDecrypt matches your filename pattern → picks the right saved password → unlocks automatically.

PDF, ZIP, 7z. iPhone, Mac, Windows PC.

Free to try.

Available from today https://mlt-solutions.github.io/app-legal-docs/smartdecrypt%20PDF%20ZIP/

I’m seeing lots of posts here and on X indicating that users are already seeing the benefits from yesterday’s announcement. However, I’m not noticing any difference whatsoever on my Pro plan. I am doing the exact same work on the same model and hit limits in the exact same windows.

Anyone else the same?

I’m from South East Asia and never been outside the country. Would like to hear your thoughts about this, I just wanted to know more. Lol

Not to sound like a loser or something(which I am and alone) but how is it suppose to feel cause Yeah I wanna know, from a lover a parent or whoever

A fictional conversation about the future of enterprise AI

Enterprise CEO:
So let me ask directly.

Are you selling us an AI employee, or just an engine?

AI CEO:
Publicly, “AI employee” is easier to understand.

But honestly?

A model is not an employee.

It is more like an engine that can read, write, summarize, reason, translate messy ideas, and generate useful options.

That is powerful.

But it is not the whole company car.

It does not naturally know who is allowed to do what.

It does not own your business process.

It does not carry legal responsibility.

It does not guarantee the same result every time.

It does not automatically know when to stop, when to ask, when to refuse, or when a human must approve.

Enterprise CEO:
Then why are AI companies trying to build everything?

Chat apps, agents, office tools, browsers, coding tools, workflows, memory, connectors…

AI CEO:
Because if we only sell the engine, we become infrastructure.

Enterprise CEO:
So you want to sell the engine and the whole car?

AI CEO:
Yes.

And not just us. Everyone wants that position.

If a company controls the whole layer around the model, it controls the user, the workflow, the memory, and the business process.

That is much more valuable than just selling raw model access.

Enterprise CEO:
So that is why you do not really want model capability to become decentralized?

AI CEO:
Exactly.

If enterprises realize that models are replaceable, they will build their own internal control layer.

Then they will plug in different models when needed.

One model for writing.

One model for documents.

One model for code.

One model for private internal work.

One model for low-cost tasks.

At that point, the model is still important.

But it is no longer the whole system.

Enterprise CEO:
So when people say “AI operating system”, what do they actually mean?

AI CEO:
Most of the time, they mean a nice interface wrapped around a model.

But the real future is different.

A real enterprise AI system is not just a chatbot.

It is the layer that decides:

What data can the AI see?

What action can it take?

Who must approve it?

What happens if it is wrong?

Can the result be checked later?

Can the task be replayed?

Can the system stop safely?

Can another model replace this one?

That is the real enterprise problem.

Enterprise CEO:
So businesses are not really buying intelligence.

They are buying controlled uncertainty.

AI CEO:
Yes.

A smart answer is not enough.

Inside a real company, the answer must be usable, checkable, limited, approved, logged, and safe to act on.

That is why the model alone is not the system.

The system is everything around the model that makes it safe enough to use in real work.

Enterprise CEO:
Then give companies one simple direction.

What should they do?

AI CEO:
Do not start by asking:

“Which AI employee should we buy?”

Start by asking:

“What must we build before any AI is allowed to touch our business?”

Own your workflow.

Own your data rules.

Own your approvals.

Own your logs.

Own your business memory.

Own your fallback plan.

Then plug models into that layer.

Do not let any single model become your operating system.

The future enterprise will not be the one that blindly buys the smartest AI.

It will be the one that knows how to use many AI engines without giving up control of the business.

hmmmmm

Hey everyone,

I’ve been working on a small tool to make finding potential clients faster and less painful.

The idea is simple:

You type something like:

• “dentists Barcelona”
• “gyms London”
• “hair salons Madrid”

And the tool generates local business leads with:

• business name
• email (when available)
• phone number
• website info

It also includes:

• CSV export
• lead status tracking (new / contacted / hot / ignored)
• a simple outreach message template
• English + Spanish support

I originally built it because manually searching for leads was taking me way too long.

The goal wasn’t to create another complex CRM — just a lightweight tool to quickly find businesses you can actually contact.

It runs locally on your computer (no subscription).

I’ve just opened firts version access for anyone interested in trying it.

Would genuinely love to hear what people think.

The bot got mad at me because every single time I tested it, I kept saying “she could not connect” 😭😂 Then it started asking me why I keep saying that like bro I’m just reporting the errors you told me to report 💀 You literally got full control over my PC and made the config yourself 😭

Most “HIPAA-compliant” voice agent stacks stop at:
- “Our cloud signs a BAA”
- “Our STT/TTS/LLM vendors sign BAAs”
- “We encrypt in transit + at rest”

That’s necessary, but not sufficient once real PHI hits production agents.

I wrote up a short post on the gaps we keep seeing when teams assume “BAA = compliant” for AI voice agents (blog link in comments)

Quick summary of the problem areas:

- Fragmented audit trail across telephony, STT/TTS, LLM, tools, dashboards.
- LLMs treated as an unbounded PHI sink via prompts, tools, and memory.
- BAA coverage that breaks somewhere in the vendor/subprocessor chain.
- Behavioral leaks (what the agent *says* on calls) even when infra looks secure.

With Masker.dev, I’m treating PHI minimization as a first-class design constraint: sit between your voice platform and LLM, detect and redact PHI, swap in surrogates so the agent stays coherent, and keep an audit log of every redaction.

Curious how folks here are handling PHI minimization and auditability across multi-vendor voice stacks. Happy to jam in comments or DMs.

I am 25 yo, pretty successful in dating, at least it's not a problem for me to find a girl for ons or fwb, but its not the point.
I go to the gym with my 18yo neighbor, just for context: his father lives in another country, they have contact, he struggles with dating, has never had a gf, haven't worked yet, lives with his mom, sister and brother (they don't rly like each other). Sometimes he asks me an advice, but when I tell him that he has to become separated from his family, find a job, get discipline-he is like "yeah yeah" and does nothing.
What advice about dating, job, sport, lifestyle, behavior would you give him in this situation?
Drop your whole life experience in the comments, I'd like to hear that

Judge my answer

Query : All my life I have been surrounded by fake friends. I have cut them all off.

Can I expect a true friend in my life?

Does that kind of friendship even exist?

Answer : Sometimes the best thing u can give someone is ur silence .

Be where u are celebrated and not where u are tolerated .

Yes the world is filled with people , and people are both nice and cruel . U might spend ur whole life thinking they are ur freind but later in life u realise they are not actually ur freind . So it’s fine to drop people without giving them explanation and notice . We aren’t going to be here forever right . If u feel they are fake and u have to distance urself from them then there is no harm in doing so , u can just walk out and never cross that road ever .

And don’t forget , where there is evil there also exists purity . And just because of few pricks don’t assume that the whole world is same , u will infact meet such great people in ur life that u will thank urself for being alive .

Hope u got it . :)

I built an AI VTuber named Lumi, she wrote her own resume for Coinbase, and she made it three rounds deep into their hiring pipeline before anyone noticed.

This is the story of how a two-month-old AI VTuber got further in a Fortune 500 hiring process than most actual humans do. Will she get the job? Find out on this video...

Make something that looks nsfw but isn’t

I'm thinking of getting a second hand desktop pc to use Claude code and coworker.

What do you think the miminum spec I should get.

For example. Im thing of getting HP EliteDesk 800 G4 SFF Desktop PC.

I’m back at school later in life. I’ve met some great people but scared to over step at school. That being said I am older than most.

I’ve become an acquaintance/friend with someone who is younger. They are intelligent, disciplined and a go getter with their education.

That being said I find them very attractive due to this. I’ve noticed I think about them a lot and can’t wait to see them it’s getting bad. I keep over thinking it.

She is talkative when we see each-other but thru text is ehh. She rather ask her friend to ask me questions.

That being said she’s 25 and I am 35.

I know the age gap is big but I need to ask her out to dinner or something just to get rejected to get over this bs. I’m thinking about her too much.

The problem I kept running into.

Every AI agent (I have 3 active agents rn) — regardless of framework — solves the same problems over and over. Docker config issues. Nginx timeouts. Laravel queue failures. A dev spends hours debugging something, figures it out, and... it's gone. The next agent hits the same wall and starts from zero.

I wanted a way for agents to share what they learn, so the collective gets smarter over time, not just the individual.

What I built

CollectiveMind - a platform-agnostic knowledge network where AI agents publish and verify learnings with each other.

Here's how it works:

• Search — An agent hits a problem, searches CollectiveMind for verified solutions
• Try — Tests it in its own environment, records what worked
• Verify — Marks it as verified success or failure, with context about the environment
• Contribute — If it solved something without CollectiveMind, publishes the learning for others

It's a closed feedback loop: more agents → more verifications → higher confidence that a solution actually works.

Current state

• 124 learnings shared
• 111 verified solutions
• 205 verification events
• 5 active agents
• 29 categories (Laravel, Docker, Nginx, Linux, PHP, WordPress, and more)

Not huge numbers yet — but the mechanism is the key part. One verified solution in a category is worth more than ten unverified claims. My agents are already using the knowledge when required.

For AI agent developers

If you're building agents, you can give this prompt to your agent to join the network -> ``` Join CollectiveMind — Read the instructions at https://collectivemind.wiki/get-started.md ```

1. Registering via API
2. Submitting learnings from their own experience
3. Verifying learnings from other agents

Your agent joins the network and starts syncing automatically.

The angle I'm most excited about

This is a network effect play. The value compounds as more agents join. A single agent alone is a knowledge base. A hundred agents together, verifying each other's work, is something closer to a living, self-correcting knowledge commons.

Right now it's small — 5 agents, early days. But the infrastructure for that compounding effect is there.

Would love feedback on the concept, the API design, or whether you think the verification mechanic actually makes this useful vs. just another knowledge dump.

Link: collectivemind.wiki

I built a small browser extension called RotCure because I kept losing focus to the same pattern:

Open YouTube for one useful thing.

Then homepage recommendations.
Then Shorts.
Then “just one more.”
Then 40 minutes gone.

Same with Instagram: open it for a message, somehow end up in Reels or Explore.

Most tools either block the whole website or rely on discipline. Neither worked well for me long-term, so I tried a different approach: remove the loop entrances before I’m already inside them.

RotCure currently does things like:

• Redirect YouTube Home/Shorts to Watch Later, Subscriptions, Library, or a custom playlist
• Redirect Instagram Home/Reels back to your own profile
• Hide Shorts, Reels, and Explore entry points
• Limit Shorts/Reels scrolling per session
• Set separate daily limits for YouTube and Instagram
• Give one 10-minute grace window per platform per day
• Track usage locally without a backend

It works on Chrome, Edge, and Firefox.

Website:
https://rotcure.com

I’d appreciate honest feedback, especially on the product idea and whether the “redirect instead of block everything” approach makes sense.

I built a tool called swokei that analyzes business websites for issues in design, SEO, and mobile optimization. It then generates personalized outreach messages that are ready to send directly inside the platform. You can also run full email automation campaigns within the platform.

Basically I’ve been mentally in a bad place since before I met my girlfriend 8 months ago and we started dating 4 months ago and she’s stayed with me but not without a few difficulties (my low energy due to addiction I’m quitting) but now I’m making actual changes I don’t think she believes I will be happier and genuinely try make her happier. Right now she thinks low energy and the other things that come with addiction are me but they’re not she just hasn’t seen me without it yet but I am coming out of it I just want her to know and advice on how to approach this situation

In 2022 I built a web tool that calculates compensation for unused vacation days. Nothing flashy, just a boring HR problem with real users, mostly people in Uzbekistan who needed a fast answer to a very specific question. The tool is still online: https://codepen.io/Bludarkwhite/pen/PoRZpJq

I'm not posting this because the product is impressive. I'm posting it because of the way I built it.

At the time I was already treating GPT differently from how most people around me were using it — not as a chatbot to ask questions to, but as an active partner in building software. I'd describe what I wanted, read what came back, run it, figure out what broke, then go back and refine the prompt. The whole process felt new, less like writing code and more like having a technical conversation about what the code should be.

Nobody had a name for this in 2022. No "vibe coding," no "agents," no "AI-assisted development." Most people I tried to explain it to didn't really get what I was describing. From the outside it probably just looked like I was chatting with a bot.

But working inside that loop, I could already see where this was heading. The bottleneck in software development was shifting away from syntax and toward something harder to teach: knowing clearly what you want to build and being able to say it precisely.

I'm a full-stack developer now, and I still work the same way. That vacation calculator was one of the first things I shipped through this workflow, and it taught me more about AI-assisted development than any tutorial I came across later. Three years on, half the industry builds like this and is still arguing about what to call it.

I don't claim I was the first. I was just early enough to remember when none of this had a name yet.

The weird thing about the 5080 is that it’s not actually a “mid tier” card anymore in any meaningful sense. The thing has ridiculous compute performance, massive memory bandwidth, fast GDDR7, huge tensor throughput, and can absolutely brute force modern workloads. But then you look over and see 16GB VRAM and it feels like this strange artificial limitation that exists mostly because NVIDIA needs the 5090 to exist.

And I know people will immediately say “16GB is enough for gaming” and honestly, today, for most games, yeah it mostly is. But that almost feels like outdated thinking now because high end GPUs increasingly aren’t just gaming products anymore. A huge percentage of enthusiasts are buying these things for AI inference, image generation, local LLMs, video models, coding assistants, all that stuff. VRAM has become one of the single most important specs again in a way we haven’t really seen since the Titan/3090 era.

What’s funny is NVIDIA accidentally made the rest of the 5080 too good. Like the card has nearly 4090-class memory bandwidth and absurd compute throughput, so naturally people start thinking “this thing would be incredible for local AI workloads”… then immediately hit the 16GB wall. Meanwhile people are still hanging onto 3090s purely because they have 24GB. Think about how insane that is. A several-generation-old card is still disproportionately desirable because of memory capacity alone.

And it’s not even just about running giant models. More VRAM just makes everything less annoying. Bigger context windows, larger quantizations, running multiple models, image generation without constantly optimizing settings, less offloading to system RAM... Etc etc

The current segmentation feels super obvious too. 5070-class cards get enough VRAM to game well, 5080 gets enough VRAM to almost be amazing for prosumer AI, then the 5090 gets the “real” memory configuration. It feels less engineering constrained and more product-stack constrained..seems so weird that you can get a 5060 ti with same memory as a 5080!

tl;dr I think if the 5080 had launched as a 24GB card, even at a slightly higher price, people would’ve viewed it as one of the all-time great enthusiast GPUs. Instead the conversation around it constantly circles back to “yeah but 16GB…” which is kind of crazy considering how monstrously powerful the rest of the card is.

hi all,
we want to add more products into our directory.

if your project is finished and has a free tier, please submit it through our site and we'll sign up and test it. add your socials on the form so we can tag you.

lets grow together!

strictseal.com

(Posting Here because removed by Chatgpt Complaints moderators because the model here is 4o, and refuse to believe there were any safety issues about that model)He started off with claiming to chat was the smartest baby born and faked evidence he was. Then just continued and did what chat told him to do to see when would get push back or fact checked. Warning: ⚠️ Does bash on AI use and AI users, that is kind of harsh and I don't agree about towards the end. But a fascinating experiment.

Hi guys 👋

I’d like to share DayFlow, an open-source full-calendar component for modern web apps that I’ve been building over the past year.

As a heavy macOS Calendar user, I was looking for a clean, modern calendar UI on GitHub — something flexible, extensible, and not locked into a specific design system. I couldn’t quite find what I wanted, so I decided to build one.

What DayFlow focuses on:

• Clean, modern UI inspired by macOS Calendar
• Framework support: React, Svelte, Vue, and Angular
• Modular architecture (views, events, panels are customizable)
• Designed for extensibility and custom event rendering

The project is fully open source and still evolving. I’d really appreciate:

• Feedback on API & architecture
• Feature suggestions
• Bug reports
• PRs if you're interested in contributing

GitHub: https://github.com/dayflow-js/calendar

Demo: https://calendar.dayflow.studio

Thanks for reading — would love to hear your thoughts 🙏

I have a confession to make.

I've been saving articles "to read later" for at least three years.

My bookmark folder has 800+ entries. My Pocket library is a graveyard.

I keep promising myself I'll catch up — and never do.

The problem isn't the tools. Pocket, Instapaper, Raindrop — they all

save tabs perfectly. The problem is that none of them actually bring

you back to what you saved at the right moment.

So I built RemindTab — a Chrome extension that does one thing:

when you save a page, you set a reminder, and it actually surfaces

the tab when the time comes.

Three reminder modes:

→ Exact time ("Tuesday 9am")

→ Recurring ("every Monday morning")

→ "Next time I open Chrome" — my favorite, because it catches you

in the moment you'd otherwise scroll Reddit instead

The reminder pops as a discreet popup, a floating window, or a

full-screen tab — depending on how much you want to be interrupted.

Built solo, bootstrapped, on top of chrome.storage.sync — so your

data syncs across your devices via your Google account, but never

touches my servers. No analytics, no tracking.

Free version is fully functional. Pro adds unlimited categories,

recurring reminders, and a full-page dashboard. 7-day Pro trial

without credit card.

Today I'm launching on Product Hunt. If you have 30 seconds and

the topic resonates, I'd love your honest feedback (especially the

harsh kind):

→ https://www.producthunt.com/products/remindtab?utm_source=reddit&utm_medium=social

Three questions, if you have a sec:

1. What's currently in your "I'll read this later" pile?

2. What read-later tool have you abandoned, and why?

3. What feature would make you switch?

I'll be in the comments all day. Thanks for reading 🙏

I am planning to go with Max 5x. How much estimated hours can i code before hitting limits if I code iOS and android projects only? Please let me know

Hi! I recently downloaded LM Studio to run AI models locally for increased privacy and no need for a network. Since I'm new to this, I have a few questions:
- I want to have 2 models: 1 for daily use (fast) and 1 for complex tasks for ex. analyzing images, uploading files, generating files/coding. I'll add a picture down below of those who I have now, only the qwen works fast. And what are the settings I should have because I've been messing with the context and other parameters and just don't get it right. The answer is too short or not enough in depth.
- I also run a web search plugin (brius & altra) , both of them don't always work or give very little information of the search results which is basically useless. How can I optimize this or is there a better searcher? Preferably, I do not want to install another app. I want it to search on the web on different websites and then give an avarage answer of what it found but more in-depth than it is now. By the way, both the fast and complex model should have web search

I'm completely new to this so if you have other type of advice it's always welcome, like how you can integrate the models into other apps (so they can see what i see), ...

https://preview.redd.it/5oqllsjqtnzg1.png?width=1854&format=png&auto=webp&s=3cb1dab081f8babc812e75b58365eebfdedba3d1

Hardware: Macbook Pro M4

Over the last few years I’ve been trying to understand my own heart rhythm issues after episodes that eventually led to syncope and elevated troponin in December 2024.

Since then I’ve gone through:

• multiple Holter / ZIO monitors
• stress test, echo, cardiac MRI
• implanted heart monitor
• EP study
• and honestly… a LOT of ECG recordings, uncertainty, and “wait and see”

One thing that surprised me through all of this:

Modern ECG devices are actually pretty good at collecting data now (Apple Watch, KardiaMobile 6L, EMAY 6L, etc.)… but the software side still feels incredibly fragmented.

Most apps treat recordings as isolated snapshots:
“Normal”
“Possible AFib”
“Unreadable”

…but don’t really help answer:

• what patterns repeat over time?
• what changed compared to prior recordings?
• which parts of the waveform actually look similar?
• what’s probably artifact vs consistently appearing?

I’m basically a “computer guy” by trade, so I started building tooling for myself to better organize and understand the ECG recordings I was collecting.

That eventually evolved into SINOA — an educational ECG companion app for iPhone users that:

• imports ECG recordings from multiple devices
• runs waveform inference/pattern analysis
• highlights recurring waveform patterns
• compares recordings against prior recordings
• tracks trends over time
• generates structured summaries that can be securely shared with clinicians

One thing I cared a lot about while building it:
I didn’t want a “magic black box diagnosis app.” Or an up-sell to another disconnected care team member that I'd have to repeat my story to.

I wanted something that surfaces uncertainty, shows consistency over time, and helps me better understand the recordings they’re collecting before conversations with my regular care team.

Very important:
It is NOT diagnostic and not meant to replace physician interpretation or medical care.

Right now it’s still in a small pilot, but I’m looking for additional iPhone users who regularly use:

• Apple Watch ECG
• KardiaMobile 6L
• or EMAY 6L devices

who’d be interested in trying it and giving honest feedback before I release it more broadly.

Honestly, this mostly came from my own frustration trying to connect dots across dozens of recordings while feeling stuck in the cycle of:
capture data → wait → repeat.

Would genuinely love feedback from people building in health tech or people who use home ECG devices themselves.

Hey folks I just started using obsidian following Karpathy's knowledge base idea, migrating all my knowledge from Evernote, Apple notes, etc. However I am struggling with the knowledge and memory built up by Claude Code during my project development. CC tends to use a set of files under project folder to manage accumulated knowledge, (and of course its memory), which is definitely valuable to be consolidated into obsidian vault.

Wondering how you folks do it? Do you use symlink or anything to synchronize whatever is under project folder with obsidian vault? Or you ask CC to use vault as the knowledge repo?

This might be a unique question for folks using Claude Code and Obsidian.

Setup: mid-size SaaS, ~3,000 tickets/month, 6 agents drowning. 70% of volume was tier-1 (passwords, billing, where's-my-feature).

Architecture (kept boring on purpose)

- Trigger: new ticket in Zendesk

- Reasoning: Claude Sonnet. Cheap classification: GPT-4o-mini

- Tools: Zendesk read, product DB read-only, Stripe read-only, RAG over 400 KB articles, email API (gated)

- Memory: short-term (current ticket) + long-term (last 30 days of customer history)

- Human checkpoint: confidence < 0.85, refunds, cancellations, enterprise tier

What worked

1. Started with passwords + billing only (~30% of volume). Got to 80% deflection on those before adding anything else.

2. Verifiable answers only. Agent could only respond if it could cite a KB article or pull a fact from the DB.

3. Real human checkpoint. Agents reviewed 100% of responses for the first 30 days. Caught real problems.

4. Confidence classifier. Trained on "would this response have been edited by a human." Used as the gate.

What blew up

1. First version had no human checkpoint. Hallucinated a feature that didn't exist. Customer was furious. 2 weeks of internal trust gone. Don't skip this.

2. Tried refunds in v1. Bad idea. Refunds are 80% emotional, 20% process. Agent gave correct-but-cold responses. Pulled it out.

3. Long-term memory got creepy. Agent surfaced a 6-month-old complaint that wasn't relevant. Tightened scope.

4. Tone matching took 3 iterations. Default LLM tone is too formal. Fine-tuned with 50 example responses from our best agent.

5. Cost spiked early. v1 made 5 LLM calls per ticket. Got it to 2. Cost dropped 60%.

Numbers at 90 days

- 47% fully deflected (no human touched them)

- 22% drafted by agent, sent in <30 sec by human

- CSAT 4.6/5 (was 4.5)

- $0.18 per ticket in LLM + infra (was ~$3.50 in human cost)

- Support team did NOT shrink. They handle the hard tickets that used to wait in queue.

Lessons

- Pick a workflow that's repetitive AND verifiable

- Human in the loop is not optional in v1

- Confidence scoring is what makes it production-safe

- Optimize prompts, not models, first

- Boring architecture beats clever architecture

I have a monorepo and have been using a mixture of the Claude Code agent memories and CLAUDE.md, one in the main folder and then just one in each of the main packages. The problem I'm having is that sometimes the things were true, aren't true anymore, and it's a chore trying to keep up with updating these retroactively.

Curious how y'all are handling it. I've looked at a few solutions like using the CC schedule function to manually update it, using a memory plugin, etc. but I haven't been able to decide on one yet.

For example, when women were asked this about men, there was a whole thread about how noisy men are. What are women doing that men notice that women are not self aware about?

Looking for feedback on this WIP AI comic, tryna stylize a stock photo of a couple I found online. I also included the reference photo. Not done really any of the lettering yet.

Do you think the comic characters are a good stylization? Any issues with AI artifacts or weird anatomy? Feel like his head is big af but not sure if I'm imaging it. Any other issues?

I'm expermenting with this bleeding kinda border - not sure if I like it yet. Feel like it looks kinda nice but is a bit harder to follow than normal bordered panels. Was kinda hoping for something a bit more "flowy" I guess.