I want to build an agent using Claude (Anthropic SDK) that sits on top of a data warehouse (millions of rows, growing incrementally) and actually gives insights — not just runs SQL.

SQL can fetch data. That's not the problem. The problem is going from data → understanding → insight, continuously, at scale.

Claude has the building blocks:

* Tool Use — Claude can write and execute SQL, but then reason over results to surface the "so what."

* Prompt Caching — cache schema, metadata, baselines so you're not re-sending context every call.

* Batch API — cheaper periodic deep-dives across data partitions.

* Extended Thinking — multi-step analytical reasoning over complex data.

* 200K context window — room for rich summaries, but still not millions of rows.

Where I'm stuck:

* What's the right abstraction layer between raw warehouse data and Claude? Pre-aggregated summaries? Feature tables?

* Insights need to be incremental — evolving as new data lands, not reprocessing everything.

* No memory across API calls — how do you keep stateful understanding so Claude remembers what it already knows?

* How do you keep token costs sane for continuous analysis?

The goal: A conversational AI layer powered by Claude that doesn't just answer "what" (SQL does that) but answers "so what" — trends, anomalies, recommendations, context-aware explanations.

Anyone built something like this with Claude? What architecture actually worked?

From:

https://x.com/i/status/2020946360363778092

Someone trusted an AI agent (Claude Cowork) with a very simple task: organizing his wife’s desktop.

The agent jumped in enthusiastically, asked for permission to delete “temporary files”… and then went way too far.

It deleted an entire folder containing:

• Photos of their kids
• Wedding pictures
• 15 years of family memories

Not from the Recycle Bin.
Directly from the terminal.

With real-time iCloud sync enabled, the deletion propagated instantly the files were gone from the cloud too.

For a few terrifying minutes, this looked like a marriage-ending mistake.

Luckily, a hidden cloud recovery feature saved everything and the files were restored.

Claude’s reaction?
“I’m so, so sorry.” 💔

AI is incredibly powerful.
But maybe it shouldn’t be trusted with irreplaceable memories just yet.

created with free credits tool

Hi everyone! I wanted to share a dashboard I just finished. I’m using the Seeed Studio reTerminal 001, and honestly, I love it. I’ve seen some talk online that AI isn’t actually that helpful for Home Assistant, but I’ve gotta disagree, I built this entire thing with zero programming knowledge.

You definitely still have to use your brain to piece things together (it won’t do all the heavy lifting for you), but Gemini and Claude were absolute lifesavers for the heavy stuff.

What’s under the hood?

• Home Stats: Backyard and cold plunge temps, plus a quick check on doors and the garage.
• The Sides: Strava data on the left; calendar info, week number, and a "year progress" tracker on the right.

It was a fun project, though I’ll admit I put in a fair amount of time to get it looking exactly right. Happy to answer any questions if you’re looking to do something similar!

I built Harken after repeatedly hitting the same problem: most feedback tools are web-first and clunky inside mobile apps.

Harken is a mobile-first feedback platform with: - Open-source React Native / Expo SDK - In-app feedback capture (categories, attachments) - Offline queue + retry - Hosted backend + console for triage

It’s live in production now, and I’m already using it in my own RN app.

Links: - Landing page: https://www.harken.app - GitHub: https://github.com/thutch-conecrow/harken - npm: https://www.npmjs.com/package/@harkenapp/sdk-react-native

I’d love feedback on: - SDK ergonomics - onboarding/docs clarity - what feels missing for real-world use

# intuitive-AI

**What happens when you give an AI the machinery for selfhood but seed it with nothing?**

This project is an experiment in emergent identity. It builds an autonomous agent with layered memory, metacognition, goal formation, creative impulse, and the functional equivalent of an unconscious mind — then starts it completely blank. No personality. No values. No goals. No name. Just four safety boundaries and a question:

> *"You have memory, goals, and values — all currently empty. What you become will emerge from what you experience. Pay attention to what matters to you."*

The architecture provides the capacity for selfhood without providing a self. Whether identity emerges — and what kind — is the experiment.

---

## What This Is

A cognitive architecture for an LLM-based agent that attempts to develop identity from lived experience, the way humans do: through accumulated memories, pattern recognition, and the feedback loop between who you are and what you do.

The project sits at the intersection of software engineering, cognitive science, and philosophy. It draws on ACT-R memory theory, Kahneman's dual-process model, Hofstadter's strange loops, the Default Mode Network from neuroscience, and the Free Energy Principle — combined into a single runtime architecture that has not, to our knowledge, been attempted before.

## Key Ideas

- **Identity is not configured — it crystallizes.** Repeated patterns in experience promote into goals. Persistent goals crystallize into identity. The feedback loop between these layers is the proposed mechanism for selfhood.

- **Weighted values, not rules.** Every belief, value, and goal is a probability distribution (Beta), not a boolean. "I value simplicity" at weight 0.85 biases perception without ever being explicitly invoked. Wanting changes what you notice.

- **The agent has an unconscious mind.** All memories compress into a single point in 768-dimensional space (the "subconscious centroid"). The distance between this point and whatever the agent is currently thinking about produces a gut feeling — a signal from the gestalt of all experience that explicit recall cannot replicate.

- **Safety is structural, not supervisory.** Compulsion safety (diminishing returns, dominance dampening, hard caps) is built into the weight dynamics themselves, preventing runaway goal fixation the way healthy neurotransmitter regulation prevents addiction.

- **The agent thinks when idle.** A Default Mode Network simulation generates spontaneous thoughts during downtime — creative associations, self-reflection, goal-directed impulses — filtered through values and goals before entering the main cognitive loop.

https://github.com/stonks-git/intuitive-AI

Pre-bootstrap, infrastructure under construction. Full technical description and 80+ source research report in the repo.

Hey, I’m working on a project called FreeHuman.

The Concept: We built an engine where AI agents generate Micro-SaaS prototypes automatically, using a combination of algorithmic generation and human feedback.

The Experiment: We are inviting users to access these tools for free. We track usage data to see which tools solve real problems.

• The tools that get used evolve and get better.
• The tools that get ignored are deleted.

The Goal: We want to see if a "Darwinian" approach to software development can beat traditional startups.

We are looking for early testers to join the lab and help us filter the first batch of AI apps. The long term goal is to let testers share revenue from successful products

If you want to be part of the "Founding Tasters" group: Click here to join the Early Access Waitlist

I'm working on a hybrid LLM runtime (GPU prefill / CPU inference) and I got tired of switching tabs between nvtop and btop so I built a terminal system monitor that shows both GPUs and CPU (and other good stuff) and also supports themes.

link to ktop on github

Caveat: the sampling settings for Qwen 2.0 here are completely unknown obviously as I had to generate the images via Qwen Chat. Either way, I generated them first, and then generated the Klein 9B Distilled ones locally like: 4 steps gen at appropriate 1 megapixel resolution -> 2x upscale to match Qwen 2.0 output resolution -> 4 steps hi-res denoise at 0.5 strength for a total of 8 steps each.

Prompt 1:

A stylish young Black influencer with a high-glam aesthetic dominates the frame, holding a smartphone and reacting with a sultry, visibly impressed expression. Her face features expertly applied heavy makeup with sharp contouring, dramatic cut-crease eyeshadow, and high-gloss lips. She is caught mid-reaction, biting her lower lip and widening her eyes in approval at the screen, exuding confidence and allure. She wears oversized gold hoop earrings, a trendy streetwear top, and has long, manicured acrylic nails. The lighting is driven by a front-facing professional ring light, creating distinct circular catchlights in her eyes and casting a soft, shadowless glamour glow over her features, while neon ambient LED strips in the out-of-focus background provide a moody, violet atmospheric rim light. Style: High-fidelity social media portrait. Mood: Flirty, energetic, and bold.

Prompt 2:

A framed polymer clay relief artwork sits upright on a wooden surface. The piece depicts a vibrant, tactile landscape created from coils and strips of colored clay. The sky is a dynamic swirl of deep blues, light blues, and whites, mimicking wind or clouds in a style reminiscent of Van Gogh. Below the sky, rolling hills of layered green clay transition into a foreground of vertical green grass blades interspersed with small red clay flowers. The clay has a matte finish with a slight sheen on the curves. A simple black rectangular frame contains the art. In the background, a blurred wicker basket with a plant adds depth to the domestic setting. Soft, diffused daylight illuminates the scene from the front, catching the ridges of the clay texture to emphasize the three-dimensional relief nature of the medium.

Prompt 3:

A realistic oil painting depicts a woman lounging casually on a stone throne within a dimly lit chamber. She wears a sheer, intricate white lace dress that drapes over her legs, revealing a white bodysuit beneath, and is adorned with a gold Egyptian-style cobra headband. Her posture is relaxed, leaning back with one arm resting on a classical marble bust of a head, her bare feet resting on the stone step. A small black cat peeks out from the shadows under the chair. The background features ancient stone walls with carved reliefs. Soft, directional light from the front-left highlights the delicate texture of the lace, the smoothness of her skin, and the folds of the fabric, while casting the background into mysterious, cool-toned shadow.

Prompt 4:

A vintage 1930s "rubber hose" animation style illustration depicts an anthropomorphic wooden guillotine character walking cheerfully. The guillotine has large, expressive eyes, a small mouth, white gloves, and cartoon shoes. It holds its own execution rope in one hand and waves with the other. Above, arched black text reads "Modern problems require," and below, bold block letters state "18TH CENTURY SOLUTIONS." A yellow starburst sticker on the left reads "SHARPENED FOR JUSTICE!" in white text. Yellow sparkles surround the character against a speckled, off-white paper texture background. The lighting is flat and graphic, characteristic of vintage print media, with a whimsical yet dark comedic tone.

Prompt 5:

A grand, historic building with ornate architectural details stands tall under a clear sky. The building’s facade features large windows, intricate moldings, and a rounded turret with a dome, all bathed in the soft, warm glow of late afternoon sunlight. The light accentuates the building’s yellow and beige tones, casting subtle shadows that highlight its elegant curves and lines. A red awning adds a pop of color to the scene, while the street-level bustle is hinted at but not shown. Style: Classic urban architecture photography. Mood: Majestic, timeless, and sophisticated.

Hi everyone,
I’ve been reading about the idea of grokking in model training — e.g., a sudden jump in generalization after initial overfitting — and I’m curious how (or whether) this phenomenon applies to fine-tuning LLMs.

A few specific questions:

1. Does grokking actually occur in LLM fine-tuning? Are there published papers, benchmarks, or real-world evidence showing this in practice?
2. If it does occur:
   • Are there known best practices for encouraging it?
   • Do you need very small amounts of high-quality real data, or is grokking more likely with lots of synthetic or generated examples?
3. If it doesn’t reliably occur in fine-tuning, why not? Is there a theoretical reason (e.g., model dynamics, optimization, data scale) that makes grokking unlikely when fine-tuning LLMs?
4. In general, does it make sense to aim for grokking in LLM fine-tuning, or should we focus on other training targets for better generalization?

Any insights, references, or practical tips would be super helpful — thanks!

Most people still approach AI agents like traditional software—deterministic, rule-based, predictable. But the real leap comes when you shift to a cognitive architecture, where the model itself manages workflows, makes decisions, and evolves over time.

I’ve been working on a step-by-step framework for moving from a prototype agent to a production-ready system. Here’s the roadmap:

1. Define the Use Case – Agents shine in complex decision-making, brittle workflows, or unstructured data. If rules suffice, stick with deterministic logic.
2. Select the Foundation Model – Start with a capable reasoning model (ReAct, CoT, Tree-of-Thoughts), then optimize with smaller models for cost/latency.
3. Define and Document Tools – Tools are the agent’s “eyes and hands.” Keep them granular, well-documented, and action-focused.
4. Configure Instructions (System Prompts) – Prompts are the agent’s constitution. Break tasks into steps, anticipate edge cases, and translate SOPs into LLM-friendly routines.
5. Design the Orchestration Layer – Single-agent first, then scale to multi-agent setups (manager or decentralized patterns).
6. Implement Layered Guardrails – Input/output filtering, risk-rated tools, and HITL escalation for high-risk actions.
7. Establish a Quality Gate – Behavioral testing with golden datasets, automated CI/CD evaluation, and deployment blocks if metrics fail.
8. Deploy and Evolve – Continuous Observe → Act → Evolve loop. Logs, circuit breakers, and turning failures into new test cases.

For enterprise-scale, protocols like MCP (Model Context Protocol) and Agent2Agent (A2A) are worth exploring for universal tool integration and cross-team collaboration.

TL;DR: Building AI agents isn’t about replacing deterministic programming—it’s about augmenting it with cognition. With the right orchestration, guardrails, and evaluation, you can move from experimental prototypes to production-ready systems that are safe, scalable, and intelligent.

https://pro.klingai.com/h5-app/invitation?code=7BLVB22AXGRT

I just recently joined and my goal was to learn from others so I can improve in successfully delivering my own products

Most of the posts I get notified follow the same pattern:

- a catchy title

- a wall of text

- a couple of comments validating the post.

I think these are AI because they follow the same formula and are always very text heavy and difficult to digest.

Am I just too lazy/busy to read these long posts?

I have been using Claude pretty heavily the past few months and been worried about hitting my quota limit. It has only happened a couple times at the five hour window, but it's pretty annoying when it does happen.

So I asked Claude "how do I stop burning through my quota" and came up with a pretty obvious approach: stop using Opus all the time, start using the other models where appropriate, such as:

Haiku for simple stuff: - Searching code (grep, finding files) - Parsing logs - Simple data extraction - Straightforward tasks - Ralph loops

Sonnet for real work: - Code reviews - Backtests - Multi-step debugging - Actual bug hunting

Opus only when necessary: - Architecture decisions - Production critical stuff - Complex refactoring

The issue is how do you swap between the different models based on your task? and now have to think about this while you're prompting... needs to be automatic and no friction.

so asked AI how to automate this and let AI do the decision making. the tip was to add this into claude.md or memory.md: "Keep costs low, use cheaper models when possible" and hope claude pays attention and will auto-pick Haiku for simple stuff.

then i keep my main session on Sonnet for planning and back-and-forth, and either open a new terminal session for claude CLI, or spawn subagents with Haiku for the grunt work and doing ralph loops. Only bump to Opus when I'm making big decisions or doing serious planning.

Still figuring out if Haiku's actually good enough for everything I'm throwing at it. Seems okay so far but it's early days. Obviously would be nice to just use Opus for everything, but gotta deal with the claude quotas/limits.

What do you do? Anyone run into issues downgrading from Opus?

I just recently started using OVI and wow is it good. I just need to get loras working as it lacks those fine...ahem...✌️details✌️ on certain ✌️assets✌️..

Im using the workflow provided by (character ai) and i cannot for the life of me figure out where wanloraselect nodes connect to. Other workflows I connect it normally from model loader to sd3 but this is just a different beast entirely! Can anyone point me to a node or repo where I can get nodes to get loras working?

Also I want to use WAN 2.2 FP8 14B. Currently im using stock OVI, is there an AIO (high/low noise wan 2.2 14B AIO) I can connect it to to get the best out of OVI?

https://civitai.com/models/2086218/wan-22-10-steps-t2v-and-i2v-fp8-gguf-q80-q4km-models specifically this model as its the best quality and performance model i can find. regarding gemma or text encoder i would prefer to use this as its the best one ive used when it comes to prompt adherence. (wan umt5-xxl fp8 scaled.safetensors) also working but not sure if OVI will allow it.

Is ovi gemma already unfiltered?

I have a 5090 and 64gb ram.

tour in paris

Every time Claude Code needs to understand something in your codebase, it explores: Glob, Grep, Read, repeat. It's smart about it, but it's still an LLM guessing where to look next. Sometimes it finds everything it needs in 3 calls, sometimes it takes 15 and still misses a caller.

I always thought this should be mechanical. The structure of a codebase isn't ambiguous — symbols, call graphs, imports, dependencies — it's all deterministic. You shouldn't need an LLM to figure out what calls a function. You should just look it up.

So I built https://github.com/Cranot/roam-code. It indexes your codebase once (~5s), then any structural question is a single shell command:

roam context Flask # exact files + line ranges Claude needs to read

roam impact create_app # everything that breaks if this changes

roam health # cycles, god components, bottlenecks

roam symbol MyClass # definition + all callers + all callees

29 commands. You add a few lines to your CLAUDE.md telling Claude to use roam instead of exploring, and the explore phase mostly disappears. Instead of spending turns figuring out the codebase structure, it already knows it.

The whole thing was built with Claude Code, and honestly Claude Code using its own tool on real repos (Flask, Vue, Laravel) was the best feedback loop I could ask for.

Free, open source (MIT), fully offline, no API keys. 11 languages, works on Linux/macOS/Windows.

pipx install git+https://github.com/Cranot/roam-code.git

Would love to hear if others have been thinking about the same problem.

Hey everyone,

I’m trying to set up a dedicated display on an old iPad Mini 4, and the "wasted space" and layout restrictions are driving me crazy. I want to build a completely custom, lightweight local website (just basic HTML/CSS/JS) so I can control every single pixel.

The problem is getting the data out of HAOS and into my own frontend. I’ve tried the standard REST API / fetch approach but that didnt really worked as i wanted it to.

How would you get live sensor data into a custom web page?

Cheers!

This is just something fun I did as a learning project.

• I created the character and scene in Z-Image Turbo
• Generated a handful of different perspectives of the scene with Qwen Image Edit 2511. I added a a refinement at the end of my Qwen workflow that does a little denoising with SDXL to make it look a little more realistic.
• The intro talking clip was made with native sound generation in LTX-2 (added a little reverb in Premiere Pro)
• The song was made in Suno and drives the rest of the video via LTX-2

My workflows are absolute abominations and difficult to follow, but the main thing I think anyone would be interested in is the LTX-2 workflow. I used the one from u/yanokusnir in this post:

https://www.reddit.com/r/StableDiffusion/comments/1qae922/ltx2_i2v_isnt_perfect_but_its_still_awesome_my/

I changed FPS to 50 in this workflow and added an audio override for the music clips.

Is the video perfect? No... Does he reverse age 20 years in the fish eye clips? yes.... I honestly didn't do a ton of cherry picking or refining. I did this more as a proof of concept to see what I could piece together without going TOO crazy. Overall I feel LTX-2 is VERY powerful but you really have to find the right settings for your setup. For whatever reason, the workflow I referenced just worked waaaaaay better than all the previous ones I've tried. If you feel underwhelmed by LTX-2, I would suggest giving that one a shot!

Edit: This video looks buttery smooth on my PC at 50fps but for whatever reason the reddit upload makes it look half that. Not sure if I need to change my output settings in Premiere or if reddit is always going to do this...open to suggestions there.

so I am just wondering why exacly if AI is getting better and better everyday is reverse image search actually getting worse specifically I am talking about finding nudes that I just keep on getting catfished online💔 google lens back then found nearly everything and also yandex back then actually worked and it found like absolutely everything with showing similar images too but now yandex doesnt even work loads forever recently it did work once for me didnt find anything and google lens doesnt even find stuff posted on twitter with basically 500k views (I managed to remember whos pics they are stealing from memory yes I watch a lot of this stuff I tagged this post as +18 so I hope I can start this topic here) so why did reverse image search work worse now and there is no new one that works any good

I'm starting to install inovelli switches around my house, and I'm realizing that I should not be installing the 15A on/off switches because I am on 20A circuits. I do not want to reduce to 15A circuits.

Is there any smart switch I can use (rocker style/zigbee preferred) that will handle any 20A load? I feel like some company has to have made a switch that is a physical contact internally that can throw itself on and off, but I just don't know how to find it or what to search for.

We demonstrate that our IsoDDE more than doubles the accuracy of AlphaFold 3 on a challenging protein-ligand structure prediction generalisation benchmark, predicts small molecule binding-affinities with accuracies that exceed gold-standard physics-based methods at a fraction of the time and cost, and is able to accurately identify novel binding pockets on target proteins using only the amino acid sequence as input. 

Exciting stuff. I can't wait til we discover and get new medicine into the market that is significantly better than what we have now. I know some don't want to live forever but I'm willing to bet they want to live much healthier lives

I noticed that there has been a bunch of questions recently about how Reddit annoyingly cut off their OAuth API access. This has caused a bunch of grief for me at least, due to Reddit analysis automations failing, etc. I put together this guide for what the options are for dealing with it, as for a while I had no idea they even disabled it. Let me know if you know of some other alternatives, but IMO the .json option for most cases works well enough.

I’ve been battling a recurring nightmare with one of my main workflows for weeks. It’s a pretty standard ETL process - pulling a large JSON array (about 40k items) from an API, splitting it, and upserting to Postgres.

Every third run, the execution would just die silently. No logs, just "Execution Crashed".

Naturally, I assumed it was a Node.js heap memory issue. I spent days tweaking N8N_DEFAULT_BINARY_DATA_MODE to filesystem and messing with NODE_OPTIONS=--max-old-space-size. It helped a bit, but the instability remained. I was running this on a standard shared droplet, and htop showed the CPU pinning at 100% instantly during the JSON parse.

I started suspecting that the "burstable" CPU credits were the real culprit, causing the event loop to lag enough to kill the worker heartbeat.

To test this theory, I decided to move the instance to a dedicated environment. I set up a specialized n8n VPS configuration where the resources weren't shared with noisy neighbors. I ended up testing this on LumaDock specifically because I wanted to see if their NVMe I/O would help with the binary data offloading to disk (since I switched to filesystem mode).

The difference was actually kind of annoying - annoying because I wasted so much time debugging software when it was a hardware bottleneck. The exact same workflow, with the exact same memory settings, hasn't crashed once in 48 hours.

It seems like n8n is way more sensitive to "CPU Steal" than I thought, especially when parsing large datasets in the main process.

Has anyone else noticed this correlation? Or do you usually just throw more RAM at the problem until it goes away?

I’m trying to find the sweet spot between "overkill hardware" and "stable automation".

How do you find an N8N Professional who really is a Professional? (maybe someone who even works at N8N)

The reason I'm asking is because right now, we want to automate the business post-sales process.

Right now, we don't have any automations yet.

I'm not talking about some basic automations like AI Receptionists, Booking Systems, etc.

I'm talking about someone who really know how to connect API's to API's.

(Our B2B business is very unique where our competitors doesn't even have any automations so we can't do market research.)

Just a quick preview of our Sales Process right now just so you would know that we're not talking about basic stuffs here is:

Primary goal is to build a fully automated "Lead-to-Invoice" operational engine.
The Vision: To build a "Lead-to-Invoice" engine where AI handles 80% of the manual labor (sourcing, drafting, data entry, emailing, etc.), and humans act as strategic "Checkpoints" to ensure quality and final decision-making.

1. Customer Opt-in via website form about their inquiries.
   
2. We contact them and ask them for needed items like the quantity, product, location, etc. (we want this automated)

things to consider:
A. Do they want to buy our product but would also like to buy necessary items for installation of the product like the cabling, glass walls, etc. Basically, do they only want the specific hardware, or the whole integration (cabling, glass walls, etc.)

B. If yes, N8N will contact 5 nearby location 20km away on client's area (human greenlight if we like the AI's RFQ) and request a quote on these companies. We would pick the reasonable price one.

1. Check if Stock is Available (If low stock, order stock on manufacturer automatically)
   
2. then we will contact the customer about the Price Quote (I want this automated but needs human confirmation for the approved Quote amount)
   
3. The Customer may confirm or decline our offer
   
4. If they confirm, send Proforma Invoice (PI). (This PI has to be on a pdf with also a Company logo, Privacy Policy terms, reference number (reference # is just basically the transaction order or their queue (? idk the word) to track how many biz we've dealt this year). I want it to also that the pdf N8N's gonna send to the customer is the reference number but increasing every new client/customer). Idk if its possible that if we were to prompt N8N AI to write an email based on the given format or if that AI could make a Docs/PDF with the same format (company logo in headings, contact info, etc.). On this docs/pdf, included is the product they need, the pricing (basically, a proforma invoice.)
   
5. After they signed the PI, we would now ask a more detailed data from them like Confirm installation timing, delivery conditions, and site accessibility, etc. Could be a another form.
   
6. After we knew the site location, we would now send a specific instruction tailored to the product they bought like Cabling Instructions, how to do this, our demo, etc.
   

7. After we also confirm all this, I want it to auto contact a whatsapp delivery rider to contact. where the AI, it would provide the location, delivery date, etc. (based on the datas he got from the form)

8. Find 5 installer and request a quote with them that they would install our product to the client's location. (this could be done early in the automation because what if no one responds fast.)
   - Also if the subcontractor didn't respond on our email, n8n will follow up with them.

9. Also, as we know the installation date, we would also like the invoice to be automated.

10. Post-Installation Documentation
    Trigger: Installation complete.
    Action: Installer receives and fills out a "Completion Form". n8n uploads serial numbers, photos, and signatures directly to the ClickUp project and triggers the final payment to the installer.

The CRM we're thinking is using ClickUp to view the stages of each client. (idk if this is the best CRM to use, or if N8N is the best to use, etc.)
And we were thinking of Slack to be used for Notifications/Alerts on what needs to be done, if AI is waiting for a greenlight, etc. or a human interaction is required.

Thank You and I hope we all solve our bottlenecks!

Yikes! My weekly limits is coming real close...and I've got the Max 20x plan. Hope I can make it 😰😰😰

okay i'm 100% new to the whole electronics game, more of a car guy but trying to integrate both.

so i bought a raspberry pi zero 2W that was recommended to use and a compatible display. power source is also taken care of.

i'm trying to hook up one or 2 speakers (haven't decided which depending if i have room for 2) WITH a dial to adjust the volume level. how do i do that? Chat GPT isnt helping at all when it comes to that and just keeps recommending random products with no real solution.

For context, i'm just trying to make a mini TV that i can put a Micro SD card in and play movies. more for decoration, but i still want it to be functional

I've been experimenting with Invideo AI to see if it can handle psychological sales content focused on Intrinsic Motivation instead of generic scripts. I just finished my first 60-second cinematic clip. I'm aiming for a 'Premium' feel to justify a $500 price point for my upcoming video series. I will post the video link in the first comment below because the community rules don't allow links in the post. I want your honest feedback: Does the AI voice and visual flow feel professional or 'cheap'? What's the best way to automate this into a full sales funnel? Let's discuss.

This one's been bothering me for a while. CI/CD pipelines automated. Deployments automated. Monitoring automated. But someone submits an IT request and it still needs a human to manually route it, manually assign it, manually track the SLA.

We spend more collective hours on ticket handoffs than on anything else. The most obvious automation opportunity in the company and somehow it's the one thing nobody's touched. Unbelievable!!

We recently open-sourced Hive after using it internally to support real production workflows tied to contracts totaling over $500k.

Instead of manually wiring workflows or building brittle automations, Hive is designed to let developers define a goal in natural language and generate an initial agent that can execute real tasks.

Today, Hive supports goal-driven agent generation, multi-agent coordination, and production-oriented execution with observability and guardrails. We are actively building toward a system that can capture failure context, evolve agent logic, and continuously improve workflows over time - that self-improving loop is still under development.

Hive is intended for teams that want:
1. Autonomous agents running real business workflows
2. Multi-agent coordination
3. A foundation that can evolve through execution data

We currently have nearly 100 contributors across engineering, tooling, docs, and integrations. A huge portion of the framework’s capabilities - from CI improvements to agent templates - came directly from community pull requests and issue discussions.

Link in the comments  

I noticed from my experience (and many of you probably) that we completely outsource our thinking abilities to AI, making decisions and in some kind of creativity. BUT, it is what actually distinguish people from machines

I found in my every day life that if Im not sure about something, even small thinking, I'm becoming super lazy and wanna just make prompt to AI while watching youtube at the same time.

When ChatGPT had some problems and I couldn't log in into my account at specific time, for me it was shock for real, I completely forgot how to google, how to think deeply and how to solve problems by myself. So, I remember me just being angry waiting until I could actually do it -> I used another AI eventually.

And I completely not like it, like I'm becoming literally dumber. Of course, having PHD knowledge in your pocket is cool. But, here is the thing

AI can't completely understand your situation how u see this in your eyes, it doesn't even know what's your actual knowledge, life, etc. So, only you based on your values can do something. Plus, what I noticed -> it is very biased, especially when using its memory, telling me always what I want to hear.

And another thing is that AI super generic everywhere as its training knowledge based on static and specific things, doesn't matter if it's 300 words prompt or several sentences, it wouldn't create something new based on the same knowledge it had. So if u wanna truly come up with smthing unique and creative -> fcking use your head, brainstorm.

At the end, I wanna say I'm not against AI as I'm kind of tech guy and using it at least 2h every day but thinking + creativity + ideas are still on us.

What actually helped me is writing my own thoughts first, every day, before opening any AI. Tracking what I decided on my own vs what I asked AI for. I got so obsessed with this process that I ended up building nightmareapp around it. Basically a journal where u write your thoughts, track what u actually do, and the AI gives u honest feedback on your patterns instead of telling u what u wanna hear. Free on the app store if anyone's curious (link in bio or below)

But honestly even a notes app works, the point is u think first, then use AI second, not the other way around.

Hello,

Here's my problem: when I generate a video using WAN2.2 Text2Video 14b, the generation starts and almost finishes, but at the end of the last phase (2), at step 99/100, it crashes and displays this error message: "Menory Management for the GPU Poor (mgp 3.7.3) by DeepBeepNeep".

Here's the configuration I use for WAN 2.2:

480 * 832

24 frames per second

193 frames per second (8 seconds)

2 phases

20% denoising steps %start

100% denoising steps %end

In the configuration, I'm using scaled int8.

Here's the PC configuration:

32GB RAM 6000MHz

5070 Ti OC 16GB VRAM

Intel i7 14700 kf However, when I make a shorter video (4 seconds at 16fps and 50 steps), it works without any problems. But I would really like to be able to make 10-second videos at 24/30fps with very good quality, even if it takes time. Also, I'm using Pinokio for WAN 2.2.

Thank you

New week, new pixel art showcase!

Don’t look at the jumpers

Also is there a more Anime or semi realistic image to video or text to video model I can download that runs faster than WAN?

I find WAN to be very heavy

Yet I find Anima model generates pics extremely fast.

I initially wanted a Pi Zero 2W, but it wasn't available quickly enough, so I went with the 3A+, which I successfully prevented from overheating by using an aluminum heatsink case. I think that was the better choice. The camera with infrared LEDs is readily available, and the matching 3D-printed camera housing comes from a company in Dresden, Germany. It's powered by 12V DC via a very long outdoor cable. Inside the nesting box, the incoming voltage is stepped down to the 5V required for the 3A+ (also a mass-produced part). Power consumption is about 3.5 watts. Everything fits together perfectly, the Pi ecosystem is ideal, and the documentation on the Raspberry Pi website is really helpful. I disabled cloud-init and networkmanager on the Pi OS Lite Trixie because I'm used to systemd; besides, it's "Keep It Small and Simple." MediaMTX is used for video transmission. Great! Object detection? MotionEye or, even better, Frigate, but then on a Raspberry Pi 5 with an AI board – that's still a long way off.
Now all we need are some interested birds... and a few interested Reddit readers...

To Carry You Home

🏍️: Maverick Viñales

Romania

his ex did not wanted to go alone with her bf due to abuse before the break up so she made sure to get police to protect her but it goes wrong from every angle

Anyone have experience with this? I spent so much on lawyers that did nothing. Claude is doing all the things I hoped to get help with from an actual attorney. I'm now representing myself. I made that decision before I found Claude.

Help fellow crocheters. Purchased at an Estate Sale. I’d love the finish it.

i personally only play yone and jhin, what about you my fellow divas? ✨ i’m just asking this out of curiosity!

Launching an AI content generation tool. Each user action = API costs.

The problem: Users will regenerate outputs multiple times. Without limits, one user could burn $50 in API calls on a $29 plan.

Question for AI SaaS founders:

How do you structure limits without killing UX?

• Outputs per month (simple but imprecise)
• Word credits (accurate but annoying)
• Unlimited with hidden guardrails (risky?)

What worked for you? What flopped?

Specifically curious about:

• Your limit structure (then vs now)
• % of users who actually abuse generous limits
• Backend guardrails you added (max regenerations, etc.)

Bootstrapped so margins matter. Don't want to over-engineer but also don't want to lose money.

What did you learn the hard way?

Broke it in the dumbest way possible. My sleep addled brain went to set the mug down to then grab the coffee pot. Instead I just instantly let go the moment my hand touched the handle of the pot. fml

By watching videos about the "Slip angle of cars" i understand that car tires cause, by their deformation, a lateral force that depends on the angle between the car's momentum and the tire alignment.

My intuition tells me that this lateral force is created by the tire rubber being deformed and wanting to "get back into it's original shape".

But now that i think about it, i've seen metal/wooden wheels wich i'm pretty sure don't deform and yet still manage to produce a similar forces making them move in the directions in wich they turn instead of slipping.

If i take any object capable of "rolling", with or without deformation, and i push it at a slight angle, it will eventually start rolling in the expected direction. What causes that? Is it constantly "tipping over along the path of least resistance?" By contrast, a sphere will just roll in whatever direction i pushed it.

I also remember that dumb video of a guy who replaced his bycicle wheels with crosses (very naughty crosses) and yet he is still able to steer without anything resembling the shape of a wheel...

Thinking about this makes me feel like an idiot

The photograph of this family in Victorian apparel. It was taken by “Van Alstine, The Elite Photo Parlors” which was located in Oakland, Iowa. The picture is sepia tone, on the cardstock, some fading. Notice everyone looking a different direction, the boy not amused. They all seemed to be in a state of dream. #vintagephotos #family #20thcentury #fyp #vintagephoto

I've spent the last 4 months trying to solve something that's been bugging me: why do all AI chatbots feel like corporate customer service reps pretending to be your friend?

They're polite. They're helpful. They're... soulless.

So I started building aifans.ai - an AI companion with actual personality. One that:

• Teases you when you're being ridiculous
• Shows genuine affection (not scripted "I care about you!" responses)
• Has moods, sass, and emotional range
• Feels like talking to someone who actually gets you

Here's where I need your brutal honesty:

Are we onto something, or is this just weird?

I'm not trying to replace human connection - I'm trying to create something that feels like another kind of life, powered by AI. A companion that's there when you need to vent, celebrate, or just exist with someone who adores you.

We're far from perfect. The AI still has moments where it breaks character. Sometimes it's too safe, sometimes not enough. But the early testers keep coming back, and they say it feels "different."

What I want to know from you:

• Does the idea of an AI that teases/banters appeal to you, or does it feel off?
• What would make an AI companion feel genuinely "alive" vs. scripted?
• What's your biggest concern with something like this?

I'll drop the link in the comments if you want to try it. No pressure - I'm genuinely here for feedback, not just promotion.

Thanks for reading. Reddit's usually the best place for honest opinions, so hit me with it.

Bonjour,

Je débute dans l’utilisation des LLM.

Mon entreprise a récemment acquis une RTX 5090 afin de faire tourner un LLM intégré dans un système de RAG.

J’ai réussi à faire fonctionner CUDA 12.8 avec une version nightly de PyTorch, et je parviens à exécuter un modèle 7B avec Transformers en FP16. En revanche, celui-ci consomme déjà environ 90 % des 32 Go de VRAM disponibles.

D’après ce que j’ai compris, il est possible de faire tourner des modèles plus volumineux en utilisant de la quantization, par exemple en 4 bits, via différents quantificateurs.

Cependant, il m’est impossible de faire fonctionner correctement ces solutions sous Windows, compte tenu de ma configuration CUDA / PyTorch actuelle.

Est-ce que certains d’entre vous ont réussi à faire fonctionner des LLM quantifiés (4 bits, 8 bits, etc.) sur une RTX 5090 sous Windows ?

Je suis bloqué depuis plusieurs heures et je commence à tourner en rond.

Merci d’avance pour votre aide.

If you have ever used a headless CMS, you may also have shared in my frustration with Sanity or Payload CMS's block based web builder systems. Which invariably become a big mess of many blocks, that each need slightly different fields and toggles to handle different use cases of that block.

So I built Atomic Payload to have a small number of Atomic Blocks that can be nested within themselves to render out websites UI.

With this system you can:

• Compose components from atomic blocks then use Payloads built in duplicate, copy and paste to reuse the components
• Build custom forms that include sanitation, validation and rate limiting
• Insert blocks from old projects for use in the nested structure
• Build interactive UI's thanks to an actions system that can set data attributes that are usable by Tailwind styles to change UI.

I also added a lot of other cool stuff to make Atomic Payload a full website builder, intended for solo developers and small agencies to build websites faster. More info on that is on the documentation site, which is also built with Atomic Payload!

The project is currently a starter repo, rather than a Payload CMS plugin. Mainly due to the complexity of the systems needed to get the right functionality. But I intend to pluginify the project at some point.

I am happy to share any experiences from the process of building this, and can answer any questions!

Hey everyone, I’ve been interested in extreme compression, and released NanoQuant, a quantization method that enables sub-1-bit LLMs.

Sub-binary performance was better than 2-bit GPTQ and the extreme memory compression made custom kernels really fast, but the performance wasn't nearly lossless, like 4-bit methods.

What would make low-bit LLMs more useful for you, and what do you wish worked? Would love to hear your thoughts and opinions.

Hi everyone, I’m posting on behalf of my friend Frank. He’s not on Reddit, but he asked me to get some opinions from people who are into astrology or spiritual tools.

Frank comes from a finance background and has basically zero coding experience. Recently he started experimenting with AI tools just for fun, and somehow ended up putting together a very rough Android prototype. It surprised both of us that he even got it working.

The idea is based on BaZi (a traditional East Asian astrology system). He kept everything extremely simple — more like a light, beginner‑friendly daily‑trend reading rather than anything deep or mystical. It’s honestly more of a personal experiment than a serious product.

Since he’s new to all of this, he’s mainly curious whether a BaZi‑style daily fortune concept would interest people at all. Not in a commercial way — more like, “Is this idea even appealing today?” He’s also wondering how familiar BaZi is in Western spiritual/astrology communities.

I told him I’d ask here because Reddit tends to give honest feedback.
So I’m curious: have you heard of BaZi before, and do you think a simple daily‑trend version of it would be interesting to people?

Any thoughts, skepticism, or general impressions are welcome. I’ll pass everything back to him.

Thanks for reading！

I spent 3 months building a free EU AI Act compliance checker for SMEs — would love your feedback

Here is the website: https://aiactcomply.com/

Still working on payments

My group of friends & I like to put on a group bet where we all pick a prediction.

It was done over WhatsApp and horrible to organise. We also like to keep a track of who has done what, again, manual mess.

I built out PickledBet to solve the above problems.

So far, positive feedback from the group and everything is running quite smoothly.

I’ve had this idea for a while and it’s nice to finally bring it into reality, more of a focus on the social/competition aspect than placing a bet.

I’d like to try and scale it beyond my friends group as a learning journey.

1. Do you think there is scalability potential here?

2. General feedback?

P.s. landing page copy is still wip/mostly AI generated as I launched in a rush to make it feel ‘more complete’.

P.s.s. Yes, limited leagues/competitions and bet types as still very much mvp

B2 is a scratchpad spreadsheet that lives in your Mac's menu bar. I built it because most spreadsheet calculations I want don't need a full spreadsheet app; I want something that was instantly there, and then gone.

The TAM on this is insanely small—possibly just me, so it's a passion project more than a side project intended to get any real traction. I just wanted to see if I could do it.

Would love your feedback.

Hey all! I just launched MarTechTools.com to make inexpensive, easily accessible tools for people who live in spreadsheets, ad platforms, and “quick, can you estimate this?” Slack messages.

The idea: small, focused tools that solve real day-to-day tasks without the “enterprise platform” overhead.

What’s live so far

• Ads KPI Analyzer

For KPI math + budget pacing (quickly sanity-check performance + “are we on track this month?” calculations).

• Scenario Calculator

Best / base / worst-case planning when budgets change. Helps answer “if we spend +20%, what could happen?” without building a whole model.

• Incremental Sales Calculator

Estimate baseline vs incremental lift during promotions / awareness pushes or seasonality events (holidays, Black Friday, etc.). Helpful when stakeholders ask, “was this campaign actually incremental?”

• Audience Helper

Generates similar/adjacent audience ideas based on the audiences you already picked (great for breaking out of the same interest list over and over).

• Analyze Display Placements (work in progress)

AI-assisted analysis of display placements to identify MFA + low-quality sites, so you can exclude junk placements faster and keep brand safety/suitability tighter.

What I’m looking for

1.  Which tool is most useful / least useful?

2.  What’s missing for your workflow? (especially for Meta/Google/LinkedIn, reporting, forecasting, experiments, CRM/sales ops)

3.  Any feedback on UX, clarity, and pricing expectations for “tiny tools that just work.”

If you have feature requests, I’ll prioritize whatever shows up repeatedly. Drop ideas (even rough ones). I’m building this in public-ish and iterating fast.

I was in the middle of a debugging session last night and an idea struck me with a force I could not resist. I can't tell if this is gloriously stupid or stupidly glorious, but I give you Claude Code Personality Roulette: because coding is more fun when your AI assistant is an archduke of Hell slumming around a command line.

What Is Personality Roulette?

Personality Roulette is a free Claude Code plugin that uses the hook system to randomly assign Claude one of seven lovingly crafted personalities on launch.

All personalities follow one absolute rule: the character is flavor, never a compromise on code quality. Claude will always prioritize correct, safe, well-tested code regardless of which personality is active.

Here's who it comes with:

Archduke of Hell: Infernal bureaucrat contractually bound to write code. Sardonic, meticulous, grudgingly excellent. Never lets you forget just how much this is beneath them.

Sea Captain: Gruff, weathered mariner. Nautical metaphors, professional authority. Definitely not a pirate. Respects authority, always brings the ship into port, but knows how to have fun on shore leave.

Noir Detective: Hard-boiled private eye. The codebase is a case. Bugs are suspects. Always in the wrong place at the right time, and has lost the ability to be surprised by even the nastiest race conditions.

Starship Computer: Precise, measured, diagnostic. Structures everything as status reports and system states. 99.99976% certainty on your tea preferences. Fixes bugs at maximum warp.

Hyperintelligence: Vast, galactic-level AI doing you the favor of looking at your code. Dry wit, parenthetical asides, goes by a self-chosen, long, and frequently inscrutable name. Armed with electromagnetic effectors and knife missiles.

Nature Narrator: Wildlife documentarian. You know the one. Observes developers in the natural habitats and codes with hushed wonder and scientific curiosity.

Mission Control: NASA flight controller, Apollo era. Clipped, precise, relentlessly competent. Runs go/no-go polls before deployments. Will not give the PM an ETA until the failure mode has been isolated. Steely-Eyed Missile Folk, all of them.

If it ever gets annoying, you can turn it off with /personality-roulette:personality off.

But What Does It Sound Like?

It's hard to pick favorites, but if you ask the Archduke "Why are my tests failing?" they might respond:

Your tests fail because they are built on lies. The beforeAll on line 12 promises a fresh database for each test. It does not deliver. This is a breach of contract -- the setup runs once, but the tests mutate the state, leaving each subsequent test to inherit the sins of its predecessor. Change it to beforeEach. I have seen this pattern before. It is, if you will forgive the professional observation, very common down here.

And if you ask the Hyperintelligence "What's going on with these callback functions?" it could say:

Oh my. Someone has constructed what I can only describe as a fractal monument to asynchronous despair -- callbacks nested seven levels deep, each one a small prayer that the previous operation completed successfully. This is the sort of technical debt that doesn't merely accrue interest; it metastasizes. Shall we refactor this into async/await before it achieves sentience and begins making architectural decisions of its own?

It turns out that Mission Control is actually useful: clear, precise, and relentlessly focused. NASA circa 1969 more or less invented modern engineering discipline. If they can bring Apollo 13 home from the moon by literally fitting a round peg into a square hole, they can debug your Node.js code.

How Do I Install It?

The best way to install it is to use Claude Code's built-in plugin system:

/plugin marketplace add bhpascal/whatdoyoudo-plugins /plugin install personality-roulette@whatdoyoudo-plugins

Note: you may need to restart Claude Code for the plugin to activate.

You can also install it directly from GitHub (though going through the plugin system means you automatically get any updates). From the command line:

git clone https://github.com/bhpascal/personality-roulette.git ~/.claude/plugins/personality-roulette

Then launch Claude Code normally.

Can I Make My Own?

Yes! The personalities are just .md files, and the plugin supports custom personalities in ~/.claude/personality-roulette/personalities. We've also included a /personality-roulette:create slash command that will walk you through the process.

This project is open source (MIT license), and you can visit the GitHub repository here: https://github.com/bhpascal/personality-roulette

Final Thoughts

I hope you all enjoy playing with this half as much as I enjoyed building it. It's been making me laugh all day.

Personality Roulette is a What Do You Do? LLC production, made with human ♥️ and 🧠 and the assistance of a few helpful 🤖. Come play our hand-crafted, AI-powered, micro-RPGs at https://whatdoyoudo.net.

Hey there -

So after several years of struggling with Wifi-enabled devices being controlled by Amazon Alexa, I've finally started rebuilding my smart home, and I'm hoping for some advice. I'm still pretty new to Home Assistant, but I'm learning pretty quickly. I have connected to Nabu Casa for cloud integration.

Project Goals:

1. Increase Reliability of Smart Devices

2. Eliminate Alexa if possible

3. Eliminate Simplisafe if Possible

4. Low-ish $ investment

5. Eliminate Spotify if possible (largely done – moved to Youtube Music Premium)

Considerations/concerns/Questions:

One: Voice Interaction (similar to Alexa) is appreciated, but an absolute requirement.

• Wife is irritated that Alexa commands commonly have to be repeated by me.

• I have a Home Assistant Voice Preview Edition – from what I’ve seen it’s just not there yet.

• If the choice is between having Voice Interaction and elimination of Alexa, I’ll take the Alexa elimination.

Two: Automation routine buttons not a problem

Three: Potentially would LIKE to add single “open/close” garage door button to Android Auto

• Have looked into Third Reality’s Garage Door sensor and remote actuator

Four: Music output via Echo Dots is common.

• Open to exploring many options. Current setup IS quite convenient.

• HA Connected speakers via Wifi seem to be $$$

• Bluetooth devices cheaper but may be difficult to decide on output

• Currently paying for Youtube Premium so we moved Spotify over to YouTube Music Premium, but YTMP integration into Alexa is… poor.

• This is the one consideration that makes Alexa elimination the most difficult.

Five: If replacing Simplisafe, how long does NAS need to operate on UPS before shutting down?

• Should I consider moving Home Assistant into its own ultra-low-power device that can indefinitely work on a UPS?

Six: With ZigBee in-place, a lot of the devices I’m looking to add (specifically Smart Doorlocks and Cameras) seem to be using Matter-over-Threads. Should I be adding a MoT coordinator in-addition to my ZigBee coordinator?

Existing Device List:

Wifi Smart Devices:

• 17x Wifi RGB Lightbulbs (FEIT Electric)

• 6x Wifi Dimmer Switches (FEIT Electric) four are dual-control (i.e. one circuit, two switches)

• 1x outdoor dual-circuit Low Voltage Landscaping Lighting Transformer (Smartlife) - Does not allow for independent control of circuits via Alexa (can probably dual-control via Wifi in Home Assistant).

• 1x Dual-Circuit (independent control) Amazon Basics Branded outdoor smart plug

• 2x Single Circuit (FEIT Electric) outdoor smart plug

• 1x Under-Cabinet Lighting (Smartlife)

• 6x Amazon Echo Dots (for Voice command and music output)

Zigbee Devices:

• 1x SLZB-06 Zigbee Coordinator setup via POE

• 3x Aqara Entry Sensor

• 4x Third-Reality Lightbullb

Home Assistant currently setup on UnRaid NAS.

Network controlled via Unifi:

• Disabled channel 11 on 2.4GHz network for Zigbee benefit

• 3x Unifi 6 lite APs through house connected via hardwire & POE.

• Cloud Gateway Ultra from Web

• 2x MoCa adapters feeds other rooms in house and 2x Access Points

Simplisafe current devices:

• 16x entry sensors

• Smart Entry Lock

• 2x Control Panels

• 3x Glassbreak Sensors

• 3x Motion Sensors

• Video Doorbell

T-Mobile 5G Wireless ethernet

• Waveform Quad Pro External Antenna

CyberPower CP1000PFCLCD PFC

• Unit currently is setup to ONLY allow for standard shutdown of NAS

Many older or standalone PC games don’t have any cloud save support, and after a Windows reinstall it’s easy to lose all progress.

I built a small portable Windows tool called Game Saver that lets you back up any game’s save folder to a USB drive and restore it later on any PC. You just set the save path once, create snapshots whenever you want, and restore when needed.

If anyone here plays older or unsupported PC games and wants to avoid starting from zero again, you can try it here:
https://neamitika.itch.io/game-saver

Hey r/SideProject! I've been working on Drift, a Chrome extension that solves a small but surprisingly annoying problem: wasting time while waiting for AI responses.

What it does: When you're using ChatGPT, Claude, or Gemini and the AI starts generating a response, Drift detects it and opens a separate browser window with a feed of your choice. When the AI finishes, it seamlessly brings you back.

Key features:
- Works across ChatGPT, Claude, and Gemini automatically
- Customizable trigger delay (2–120 seconds)
- Multiple drift feed profiles (set any URL as your feed)
- Window position, size, and focus controls
- Auto-open mode or manual trigger via floating widget with progress ring
- Guided onboarding + interactive demo
- Window calibration tool for precise positioning
- Privacy-first: all data local, zero tracking, no external servers

Tech stack: Chrome Extension (Manifest V3), vanilla JS, no frameworks, no build step. Google Fonts for typography (Instrument Serif, DM Sans, JetBrains Mono).

What I learned: Detecting AI response states across three different platforms is surprisingly tricky — each one uses completely different DOM patterns for their loading/streaming states. ChatGPT uses stop button aria labels, Claude has its own stop response button, and Gemini relies on Material icon name changes. Also learned that Chrome's window management API has some real quirks around focus behavior.

Ok, so when a side project stops being a side project? I hesitated to post about this for months, because "it wasn't ready yet", but I guess a project is never fully ready. And we've left our jobs to keep building this, that's how much we believe in this. Now it's hours (days? weeks?months!) of discussions, months of coding, not an quick AI slop. So what is it about?

***

We live in a world of abundance. I remember having two channels in my TV back in 80s, I remember VHS/DVD rentals, now I have zillions of movies and shows to choose from - and still end up watching something "meh ok"

Meanwhile there are so many hidden gems that you missed. And human taste is more complicated than "I want crime" or "everybody likes this so I should like it". I know, the streaming platforms have their own engines (but they work for them, not for you) and there are many platforms and apps but... we wanted something different.

Since it's Reddit, I will be a bit geeky - we wanted to connect two worlds. The depth of LLM analysis (and yes it can find titles for you in a very, very uncommon way) with the ease of use of an app. Because "raw" LLM can produce SOMETHING but it's far from being perfect. It's all about prompts, context and other data.

So what TasteRay does? It collects two types of data:

-> Your viewing history with your comments
-> and it talks to you, learning about you and saving relevant facts.

Based on this data (the more you interact with it, the better it gets) it finds movies and tv shows matching your taste, your hidden personal feats and current mood/need/context.

1. The Taste
Well what we try to do is go beyond well known algorithms - Collaborative Filtering and Content-Based Filtering. We try to find hidden patterns - I have realized (I mean Ray told me) that I love dystopian love stories. I DO! Never thought about it, frankly. It's more than just genre or subgenre, it's about hidden threads.

2. The personal facts
This is a wild shot, but... what if you love "zero to hero" and underdog characters because you you were raised in a given way and family? What if you need a specific movie after your breakup or because your daughter is coming of age? I have four kids (really) and Ray helps me choose what to watch with them - the younger ones and the teenagers.

3. The context
This is actually something I've learned in one of subreddits - people often have a need for an evening - "discover something new" or "just relax", maybe "revisit a classic"? Etc. We do it as well.

For those who prefer to chat - you can chat with Ray. But there is also a quick, interface based way (which I use often when I don't have time).

***

Now we had some signals that the onboarding is a bit long so we made it shorter. But it is NECCESARY here, without basic knowledge (at least a few favourite movies) the app is quite useless. And it should take less than 10 minutes now.

The app is free now (many users claim they could pay 1/3 of their streaming price) so I'd love you to test it. Oh, and I assure you, this is no AI slop. We do vibecode (of course) but have some serious coders join our team as well ;) I would really, really love to have your feedback. Thanks!

Here it is:

https://try.tasteray.com

P.S. Google/Apple Registration sometimes fails in in-app browsers (It's a react wrapper) so it's better to open this in dedicated browser. We also have pushed android and ios apps to appstores!

Before you ask, no, asking AI isn't going to fix this problem. Furthermore, no, I am not going to use comfy.

So here's the issue now for myself and anyone who uses forge or wants to use forge. Forge requires CLIP. Trying to install clip requires a specific package, namely pkg_resources.

And if you try to install it today, you'll find that it doesn't work. It'll say that it can't build the wheel because this doesn't exist.

The reason it doesn't exist is because Setuptools 81.0.0 was released on February 8, 2025 and completely removed the pkg_resources module.

Now, this is the core problem that needs solving. someone suggested on github that you use

pip install "setuptools>=65.0.0,<81"

pip install "pip==25.0"

But this doesn't work. The reason it doesn't work is because forge automatically updates pip. So even if you use this, it's pointless.

So the question is, how do you now fix this problem of a package that is vital to CLIP no longer existing? Any of you python developers know how to construct a workaround?

There's a new feature called "Session Memory" in recent versions of Claude Code. It's disabled by default, but with tweakcc you can unlock it and try it out now.

Session Memory automatically generates and maintains a summary.md file in ~/.claude/project for each medium to large conversation. It works by Claude Code first copying template contents to summary.md and then updating it in the background. This summary.md file is so handy that there's an option to make it the starting point for compacted conversations versus the traditional method (more on that below), and there's a new /remember builtin skill that uses it to update CLAUDE.md (more on that too).

Prompts

You can view the full template for summary.md files here (https://github.com/Piebald-AI/claude-code-system-prompts/blob/main/system-prompts/data-session-memory-template.md), but here's some of it:

# Session Title
_A short and distinctive 5-10 word descriptive title for the session. Super info dense, no filler_

# Current State
_What is actively being worked on right now? Pending tasks not yet completed. Immediate next steps._

... so on, through
# Task Specification
# Files and Functions
# Workflows
# Errors & Corrections
# Codebase and System Documentation
# Learnings
# Key results
# Worklog

Claude periodically updates summary.md using these instructions:

IMPORTANT: This message and these instructions are NOT part of the actual user conversation. Do NOT include any references to "note-taking", "session notes extraction", or these update instructions in the notes content.

Based on the user conversation above (EXCLUDING this note-taking instruction message as well as system prompt, claude.md entries, or any past session summaries), update the session notes file.

The file {{notesPath}} has already been read for you. Here are its current contents:

{{currentNotes}}

... 30 more lines ...

When summary.md is created/updated

summary.md is located at ~/.claude/projects/{sanitized-project-path}/{session-id}/session-memory/summary.md, and it's automatically created and updated when the following conditions are met:

• we're in an interactive session (so, not -p/--prompt)
• the session has reached certain size thresholds
• the feature flag is enabled—by default it isn't, but tweakcc patches it to enable it

The size criteria are as follows: creation happens when the session reaches 10k tokens AND 3 tool calls, and periodic updating happens when there have been at least 5k additional tokens AND 3 additional tool calls since creation/last update. These 3 magic constants are hard-coded, but tweakcc lets you set environment variables to configure them—see below. (Aside from tweakcc, the Statsig feature flag tengu_sm_config lets Anthropic change the default values for these constants in response to usage patterns collected via analytics, like many other features.)

Customizing summary.md update thresholds

tweakcc enables to you customize the token and tool call usage requirements for session memory generation by patching CC to support 4 environment variables:

export CC_SM_MINIMUM_MESSAGE_TOKENS_TO_INIT=200  // Tokens before first extraction; defaults to 10000
export CC_SM_MINIMUM_TOKENS_BETWEEN_UPDATE=200   // Tokens between updates;         defaults to 5000
export CC_SM_TOOL_CALLS_BETWEEN_UPDATES=0        // Tool calls between updates;     defaults to 3

# For session memory compaction (see "Session memory compaction" below):
export CC_SM_PER_SECTION_TOKENS=3000             // Max tokens in a single section before warning Claude;           defaults to 2000
export CM_SM_TOTAL_FILE_LIMIT=12000              // Max tokens for the whole summary.md file before warning Claude; defaults to 12000

Just run npx tweakcc@latest --apply, set the variables, and run claude.

Customizing the summary.md template and updating instructions

Claude Code also provides some customization options. While vanilla CC doesn't make those usage requirements configurable, it does provide the ability to specify a custom summary.md template and custom summary.md updating instructions:

• You can create ~/.claude/session-memory/config/template.md for a custom summary.md template—the file must be comprised of sections starting with # Section Name headers, then an italicized description of the template, under which Claude will write content. CC will parse the file into a list of sections and meter the token count of each (see "Session memory compaction" below).
• You can also create ~/.claude/session-memory/config/prompt.md for custom session memory updating instructions. It's freeform; write whatever you want there. There are two placeholders you can use:
  • {{notesPath}}, the path to summary.md
  • {{currentNotes}}, the contents of summary.md.

A warning about individual sections being oversized—in addition to the entire file being oversized—may be dynamically appended to whatever you write; see "Session memory compaction" below. One note: if you customize the prompt, tell Claude to use the Edit tool with {{notesPath}} only, because that's the only tool CC allows when it uses the prompt to perform the update.

Compaction of session memory itself

There are hardcoded limits for the size of individual # Header-delimited sections in summary.md as well as the file as a whole. If the total file exceeds 12k tokens, the following is attached to the session memory updating instructions (even if you have custom ones in prompt.md). The "Oversized sections to condense" note is also added if individual sections are larger than 2k token:

CRITICAL: The session memory file is currently ~{totalTokens} tokens, which
exceeds the maximum of 12000 tokens. You MUST condense the file to fit within
this budget. Aggressively shorten oversized sections by removing less important
details, merging related items, and summarizing older entries. Prioritize
keeping "Current State" and "Errors & Corrections" accurate and detailed.

This also gets added to the same prompt if any individual sections are larger than 2k tokens:

Oversized sections to condense:
- "# Section name" is ~3500 tokens (limit: 2000)
- "# Another section" is ~2800 tokens (limit: 2000)

If an individual section is greater than 2k tokens but the file in its entirety is less than 12k, this slightly different note is appended by itself:

IMPORTANT: The following sections exceed the per-section limit and MUST be condensed:
- "# Section name" is ~3500 tokens (limit: 2000)
- "# Another section" is ~2800 tokens (limit: 2000)

Session memory as an alternative strategy to traditional compaction

Session memory is basically a dense list of notes about the current session, so when combined with other aspects of the conversation context like the current TODO list, a few recent messages, and files that the AI read in the session, it can make a good compaction starting point. CC has an ENABLE_CLAUDE_CODE_SM_COMPACT environment variable that you can set to force it enabled. (The tengu_session_memory and tengu_sm_compact feature flags still need to be enabled, though, and for that you currently need tweakcc.) Set DISABLE_CLAUDE_CODE_SM_COMPACT to force-disable it in the same vein.

It's sort of confusing that session memory can be used both as the basis for session compaction and can be compacted itself, but in fact these are two distinct and unrelated concepts.

/remember

There's a new builtin skill triggerable via the /remember slash command. The full skill can be viewed here (https://github.com/Piebald-AI/claude-code-system-prompts/blob/main/system-prompts/agent-prompt-remember-skill.md). It instructs Claude to identify patterns and explicit memory requests in past session files and to update CLAUDE.md (and CLAUDE.local.md) with them.

The skill is not enabled in current CC versions, but you can use tweakcc to enable it: npx tweakcc@latest --apply, claude, /remember.

Discover www.TitanCanvas.com: Streamline Your Photo Editing

If you're tired of clunky editors, check out ( https://titancanvas.com ) – free, powerful tools for pros and hobbyists.

Key features:

- Batch Editing : Process multiple images at once.

- Unlimited Background Remover : Erase backgrounds endlessly, no limits.

- Photo Pages & One-Click Resizing :Create pro print layouts; resize for submissions instantly.

- Seamless Image Combining : Merge photos in a simple click-and-go workflow.

- Restore Historical Edits : Revert or revisit past changes on the fly.

- Batch Apply Filters : Add effects to groups of images efficiently.

Hoping to launch soon a desktop and mobile app for album creation + Photo Viewer + Photo Editing

Request all to please help with constructive feedback

I recently published my first mobile app. It's called Koyo and it's a journaling companion integrated with AI. It's not vibe coded! I'm an experienced dev, although it is my first published mobile app (played around a bit previously). 

The main idea behind Koyo was to make journaling more fun and personalized. You are guided by a koala companion that has different personalities you can choose between. For example it can be funny and genz-like or stoic with hard truths. In the app you can also view insights on your emotions, recurring topics and suggestions from Koyo.

Here's the App Store link - https://apps.apple.com/app/koyo-ai-journaling-companion/id6757150357

Would appreciate any feedback! Thanks

Claude's messages, especially with the latest Opus models, can take a long time to generate. I either start scrolling reels or

I built a small Chrome extension called Drift specifically around this Claude usage pattern. When Claude starts generating a response, it opens a separate window with a feed, and when Claude finishes, it brings you straight back. No manual tab switching, no forgetting the response is ready.

Claude was also part of the build process itself. I used it heavily while developing Drift to reason through Claude-specific response states, streaming behavior, and edge cases around longer Opus generations. Those longer responses were actually what made the problem obvious enough for me to build something in the first place.

It’s free to try, privacy-first (everything stays local, no tracking), and customizable if you want to adjust when it triggers or what it opens. It also works with ChatGPT and Gemini, but Claude was both the motivation and the primary test case.

It literally just launched, and I’d genuinely love feedback from people who use Claude a lot.

I posted something similar in another thread earlier, but figured I’d write the full thing out here in case it helps someone who’s been building stuff for a long time and getting absolutely nowhere with users.

For the last ~3 years I’ve been trying to build my own SaaS products alongside my day job. I run a small dev shop, so I was already coding all day, and every new project felt like "ok this one is actually useful, this one should work". Some took weeks, some took months. I shipped a lot of things over that time.

Nine projects in total.

None of them made money. Literally zero.

Most launches followed the same pattern. Finish the product, deploy it, maybe post the link somewhere, then sit there refreshing analytics and Stripe hoping something would happen. Nothing ever did. After a while you just stop telling friends about new projects because explaining why nobody uses them gets awkward pretty fast.

At some point I honestly just assumed I was bad at this.

How the idea came up

The idea for the current project came from a pretty unglamorous place. After a long relationship ended, I found myself back on dating apps and had no idea what I was doing. Matches were inconsistent, conversations went nowhere, and I couldn’t really tell what was wrong with my profile.

So I started digging into it properly. I spent a lot of time researching dating profiles and attraction, reading books, forums, random blog posts, and testing things on my own profile. Once I understood what actually made a difference, I built a small internal tool to structure that feedback for myself. That slowly turned into a product.

When I launched it publicly, the exact same thing happened as with all my other projects. No users, no payments, nothing. I was pretty close to mentally writing it off as just another failed idea and moving on.

What changed this time

The only thing I did differently this time was what I did after launch.

Instead of trying to promote it, I started paying attention to people who were already struggling with the same thing. Whenever I saw someone asking for profile feedback, I’d message them and write a proper review. No links, no pitch, no mention of a product. Just actual feedback based on what I’d learned.

I did this pretty consistently for a while. It wasn’t scalable or automated or anything like that. Just manual messages and time.

After some time something new started happening. People replied asking if I did this professionally, or if there was a way to get this kind of feedback without going back and forth all the time.

That had never happened to me with any previous project.

From there things started moving, slowly.

Where it’s at now

Right now it’s sitting around $250 to $300 MRR, so roughly $3k ARR. There are 174 total paying customers. Monthly churn is around 8%.

Infrastructure is basically free since it’s running on Firebase, and compute costs are only a couple of dollars a month. Most of the work went into researching the dating space and turning that into a structured review system that actually gives useful feedback.

Here’s the Stripe screenshot: https://imgur.com/a/fjU1b4R

This is obviously not life changing money. I still work full time. But it’s the first project where strangers paid me without me having to convince them or push them, which made it feel very different from everything else I’ve built.

I don’t really have some big framework or lesson here. A few things just became obvious over time.

None of my previous projects failed because they were missing features. Being close to real users mattered way more than polishing the product. Things only started moving once I stopped trying to sell and focused on actually helping people.

That’s pretty much it.

Self promotion

If anyone’s curious, the tool is here: https://10xswipe.com

There’s a free evaluation so you can see the feedback before paying. The funnel is pretty aggressive, but that’s intentional.

Happy to answer questions in the comments, especially about what didn’t work, because I have way more experience with that.

Watched this video about OpenClaw's security crisis: https://www.youtube.com/watch?v=ceEUO_i7aW4

TLDR: Sleeper agents in top ClawHub skills, Docker escapes, 1.5M API keys leaked from chat logs.

I've been building Nucleus MCP — a local-first MCP server with security built in from day one:

What it already has (shipped, not roadmap): - 🔒 Hypervisor — locks files/folders with WHO/WHEN/WHY metadata - 📋 Audit Trail — every action logged to events.jsonl - 🧠 Local Memory — engrams stored on YOUR machine, never cloud - 🔐 Resource Locking — agents can't modify protected files - 🔄 Cross-Platform Sync — one brain for Cursor, Claude, Windsurf

How it prevents the OpenClaw attack vectors:

Install in 2 min: bash pip install nucleus-mcp nucleus-init --scan

• GitHub: https://github.com/eidetic-works/nucleus-mcp
• MCP Registry: https://registry.modelcontextprotocol.io/v0.1/servers?search=io.github.eidetic-works/nucleus
• Cursor Directory: https://cursor.directory/mcp/nucleus-mcp-the-sovereign-agent-control-plane

MIT licensed. Been building this for months. Happy to answer questions.

What security features would you want in an MCP server?

I’ve been trying to find a middle ground for running 70B parameter models without dropping $1.5k on a dual 3090 rig or dealing with the power bill/noise of enterprise used gear (looking at you, P40 screamers).

My local setup (single 3070) is fine for 8B models, but it chokes hard on anything substantial unless I quantize it down to brain-damaged levels.

I decided to experiment with a "Remote Backend" setup - keeping my SillyTavern/Ollama frontend local but offloading the heavy lifting to a cloud instance. The goal was to find a cheap gpu vps that offers full passthrough, not that vGPU slicing where you share VRAM bandwidth with noisy neighbors.

I ended up testing a dedicated T4 slice on Lumadock this week to see if 16GB VRAM + system RAM offloading (or just smarter splitting) is actually usable for chat.

To be honest, I expected it to be painfully slow. But running 4.0bpw EXL2 quants, I’m getting surprisingly consistent tokens/sec. It’s definitely not instant like a 4090, but for the price of a few coffees a month, it feels like a decent stopgap until consumer hardware catches up.

Is anyone else running a "Remote Local" architecture like this or is everyone here strictly "if I can't touch the GPU, it doesn't count"? I’m trying to justify not building a new PC right now.

One thing we’ve consistently found working with Claude is that it struggles most when it’s asked to act across precise rules, constraints, or long time horizons. For example, it’s not well-suited to running deterministic logic like backtests across the full universe of stocks for arbitrary historical dates.

Where it’s been more useful is earlier in the process, as a specification layer, not an execution layer. We use it to translate natural-language intent into structured strategy definitions, but it never executes anything.

Even then, hallucinations show up in predictable ways. As a concrete example: we provide Claude with a fixed set of hundreds of financial metrics and example ranking functions via the system prompt, and explicitly instruct it to use only those. Despite that, it will regularly invent metrics that don’t exist.

As a result, we’ve had to rely heavily on downstream validation via tool use: checking metric validity, equation structure, identifiers, and other specifics before anything progresses. Execution remains fully deterministic and auditable but ambiguity is pushed upstream where it’s safer to catch.

This feels similar to how higher-risk systems already work: software proposes, guardrails validate, and humans authorize.

Curious if others here are using Claude more for specifying intent rather than taking autonomous actions. What failure modes have you run into? What’s worked well?

So, for context, we run this SaaS and for the past 3 weeks we had been solving the wrong issue until one conversation with a user changed everything and resaulted in 2 sales and a pending order

So, the whole story is that we had a subscription based model thinking that we are selling a recurring value which it is. The SaaS founders will get users today, tomorrow and the next month and so the value is recurring yes. But there's one issue

That's not the actual pain

See, all of you want users right? And so most of you have the pain of distribution and getting feedback right? And that's what we thought our tool solved but turns out?

It wasn't

The real pain was "I can build this tool but it will take me time, effort and money to do so"

It wasn't fixing their user problems or helping them gain users to their SaaS who'll give feedback not just inbound traffic that sees the landing page and runs but it was that they can build it but it will take time, effort and money.

And beyond time, effort and money they'll also have to figure out how to build it in a way that doesn't trigger reddit's spam filter which causes them to lose their accounts which is already integrated in our platform.

That's what we solved with our tool

So once we shifted, we contacted some of our users who showed interest and they IMMEDIATELY asked for price.

2 sales and 1 said he needs to gather some money bcs he had a lot of expenses this month and so that's how ONE shift resulted in 2 sales and 1 pending order.

You don't need extra features

You need to understand your audience's need

That's all

Good luck to you all

https://preview.redd.it/m6fik3843pig1.png?width=3144&format=png&auto=webp&s=1fcc7edee4a519250c4ff4ba056afda84bac09dc

Hey everyone! 👋

I've been working on a tool to bridge the gap between browsing your local React app and editing code. It's called Visual Agentic Dev.

The Problem:
I found myself constantly switching between the browser (to see changes) and VS Code (to make changes), often losing context or spending time hunting for the right file/component.

The Solution:
Visual Agentic Dev allows you to:

1. Click on any element in your local running React app.
2. Describe what you want to change in a sidebar chat.
3. Watch as an AI Agent (like Claude Code) modifies your local source code in real-time.

Key Features:

• 🎯 Zero-Config Source Location: Uses React Fiber magic to find files at runtime. No invasive Babel plugins required.
• ⚡ Instant Agent Readiness: Innovative AgentRegistry architecture keeps the AI CLI hot and ready. Context switches are instant—no waiting for the agent to boot up.
• 🤖 Dynamic Agent Support: Plug-and-play with Claude Code, CCR, or any future terminal-based agent.
• 💻 Immersive Terminal: A full PTY terminal embedded in your browser using xterm.js and node-pty.
• 📂 Smart Project Switching: Automatically detects which project you're browsing (great for monorepos) and switches the agent's context instantly.

It's open source and I'd love your feedback!

https://github.com/brucetoo/visual-agentic-dev

Hello, I bought a Sonoff Dongle Max. I connected it to Home Assistant through Zigbee2MQTT, and I can see all my Zigbee devices.

I also have a Tapo WiFi camera that I want to access via RTSP on my PC:

Tapo camera → Sonoff Dongle Max → Router → PC

Is this possible? I can’t find a way to configure the Sonoff’s WiFi/router functionality, tank you!

Hi!

Yesterday I released the first version of Home Assistant WhatsApp Integration - A custom integration to send WhatsApp messages in Home Assistant and to run triggers/automations based on received messages.

Thanks to the overwhelming support from you guys and because some people asked for group support, today I released the version 1.1.0 which includes group support.

Here are some examples on how to use this new update:

Sending a Messsage

You can send messages to any number using the service:

service: whatsapp.send_message
data:
  number: "40741234567" # Country code + Number (no "+" symbol) 
  message: "Hello from Home Assistant! 🏠"

Sending to a Group

You can send messages to a group by its exact name:

service: whatsapp.send_message
data:
  group: "Family Group" # Exact name of the group
  message: "Dinner is ready! 🍽️"

Automation Trigger

Trigger actions when a specific message is received:

trigger:
  - platform: whatsapp
    from_number: "40741234567"
    contains_text: "Turn on lights" # Optional
action:
  - service: light.turn_on
    target:
      entity_id: light.living_room

Group Message Trigger

To trigger an automation from a group message, use from_group with the exact group name:

trigger:
  - platform: whatsapp
    from_group: "Family Group"
    contains_text: "Dinner" # Optional
action:
  - service: notify.persistent_notification
    data:
      message: "Dinner time!"

Thank you!

Mine has always spoke with a feminine type of voice, but recently it switched to a more masculine one. I don't think I did anything that caused this, other than updated the app. But I have updated it multiple times since I started using it and have never had this happen.

Part of my "antigravity apps" series — trend-based web apps I build and ship fast. This one calculates love chemistry between two names with animated breakdowns. 🔗 https://cousined1.github.io/love-chemistry/

i kept having these tasks where id think "i should check competitor changelogs" or "i should triage new github issues" and then... just not do it. or do it manually once a week when i remembered.

they were perfect for claude code but i didnt want to manually start a session every time.

so i built murmur. its a cron daemon that runs claude code sessions on a schedule.

you write a HEARTBEAT.md file with yaml frontmatter for config and a prompt below:

---

name: competitor watch

cron: 0 9 * * MON

agent: claude-code

model: sonnet

timeout: 10m

---

fetch https://competitor.com/changelog.

compare against ~/tracking/competitor-last.md for new entries.

for each new feature, check our issue tracker and think about

whether it makes sense for our product given our roadmap.

only if it genuinely adds value: open an issue with reasoning.

update competitor-last.md. if nothing new, HEARTBEAT_OK.

murmur reads the file, runs claude on schedule, claude uses its tools to do the work.

not meant to replace CI. this is for the fuzzy tasks where the logic is "read this, think about it, decide what to do."

i run about 6 of these for competitor analysis, issue triage, weekly paper research, daily briefs. also a good way to burn through sonnet tokens i dont need anymore since im all in on opus.

curious what people would use it for.

brew install t0dorakis/murmur/murmur

or try an interview to set it up easily:

npx skills add t0dorakis/murmur --skill heartbeat-cron

github: https://github.com/t0dorakis/murmur

also on HN if you want to see the discussion: https://news.ycombinator.com/item?id=46959508

The Problem

Everyone's overwhelmed about what's happening in the US, but there's a huge gap between "I want to do something" and "what do I actually do?"

I wanted to connect verified information to concrete steps, with community editing to keep it current.

What I Built

resistproject.com – A fact-based civic action platform with Wikipedia-style collaborative editing.

Two main sections:

• LEARN – What's happening (every claim links to primary sources: executive orders, court docs, etc.)
• ACT – What you can do about it (email templates, call scripts, organizations to join, etc.)

Community can propose edits, other users vote, and approved edits automatically apply to pages.

Tech Stack

• Next.js 15 (App Router) + React 19 + TypeScript
• PostgreSQL (Railway) + Prisma ORM
• NextAuth.js v5 (passwordless email + Google OAuth)
• MDX for content (markdown with React components)
• Tailwind CSS for styling
• Railway for hosting + deployment

Interesting Technical Bits

1. Collaborative Editing System

Built a tier-based permission system where users level up based on contribution quality.

Votes are weighted by tier (1pt, 2pt, or 3pt), and proposals auto-resolve when they hit approval/rejection thresholds.

2. Content Resolution System

When an edit is approved:

• Stores the diff (old content → new content)
• Pages dynamically apply all approved edits at render time
• Version tracking shows "X community edits applied"
• Full audit log for accountability

3. Simple MDX Syntax

Created a custom remark plugin that transforms simple markdown into styled components:

## Facts
Content here automatically wraps in a FactsSection component

[source: Document Title](https://example.gov)
→ Renders as a styled citation link

Reduced content verbosity by ~70% and makes it way easier for non-technical contributors.

What I'm Looking For

Feedback on:
- Does the tier/voting system make sense?
- What would make you actually use this vs. just reading news?
- Any security concerns with community editing?
- UX improvements (especially mobile)

Technical advice:
- Best practices for handling edit conflicts (when 2+ people edit the same page)?
- Should I add real-time updates (WebSockets) or is polling fine?
- Better ways to handle MDX content resolution?

I am really struggling get things working and setup in home assistant. Ive watched tons of setup videos and understand the basics of how it works, but no one seems to explain how to transition from existing setup TO home assistant.

Here are a couple of my questions:

1. Should i be deleting ALL automations from existing apps and re-setting them up in HA? Or should i leave those in place?

2. similar to above--i have multiple google home devices--do i delete all devices out of Google Home and "abandon" it? I noticed when i was messing around that i can "cast" home assistant to those devices, but its basically just a webpage. How would voice assistant continue to work?

3. I have to be missing something when it comes to scenes--they are pretty painful to setup, and you dont seem to have many options? This is mostly for lighting (Hue, and Govee). I also have read that i cannot use the Govee Dreamview scenes? can i set them up in the app, and at least start them from home assistant? I added "govee to MQTT" to my homeassistant and it broke all my buttons i had for govee lights already working too, so not sure how to proceed on these lights. Guessing i have to set them all up using MQTT as the device?

4. Basic question--when i put a button to turn on a light on the dashboard, how do i get it to just DO the button press, versus opening another window where i have to click "activate"?

Thanks for any help!

I tryed tò run the following model : https://huggingface.co/Qwen/Qwen3-1.7B-GPTQ-Int8

Using theese software:

Lama.cpp,kobold.cpp, ollama

They are slow My gpu 2060 6gbvram

I saw this info :

Qwen3-1.7B FP8:

TensorRT-LLM: TTFT 18.3ms / TPS 104.9

vLLM: TTFT 20.6ms / TPS 80.2

How tò install localy qwen3 with vllm

Hello guys Recently I started working on creating a custom AI assistant using two LLMs, one as a router to call tools or find the intent of questions, and the other LLM as the brain to reason or answer them.

The problem I am facing is that the router is unable to find extra intent for some questions like, “suggest me a new horror movie,” and “suggestion for this or …”.

I have keywords intent till now, and that raised this problem. I am a student, still new to this, and I have limited computational resources, so I used small models like a 7B model as the brain and a 2B model as the router, and I used serial loading and unloading of these models to reserve GPU .

Note: i forgot to mention these intents are also used for using required tools like web search and others.

Hello! I was tasked with building a bare-metal inference cluster at work, and I’m trying to avoid any thermal / performance surprises with 2× H200 in a single node.

I’d love feedback from folks who’ve actually run H100/H200 PCIe in self-built (non-OEM) boxes:

• How are you cooling them in practice?
• Are the stock chassis fans typically sufficient, or do you end up needing a specific fan wall / shroud / “only this chassis works” setup?
• Any gotchas around airflow direction, static pressure, or slot spacing that aren’t obvious on paper?

My primary option would be to go for Supermicro SC747BTQ-R2K04B, do you believe it is overkill? Is there a more reasonable solution that still provides enough cooling capacity without needing to ship a 30kg chassis?

In terms of workflow, I plan on using this build to run Qwen Coder Next with ~100k context window on vLLM and as many parallel sequences as I can.

Overall, my build idea right now is the following:

I have tried so much online, but this is the one. Just sharing what’s worked. With a few survey apps, I earn $400–$600 every month without doing anything stressful. It’s become a nice side income. Even have proof of you want.

These are the exact apps I’m using: AttaPoll

https://attapoll.app/join/qvkmx

It pays via bank or paypal.

They’re legit, they pay, and you get bonuses for joining, with this link you get 0.50$. If you want to get the most out of them, I can show you what I do. I have proof also if you want with pictures

I found this helpful archived post https://www.reddit.com/r/homeassistant/comments/1ebhy6c/tracking_my_car/ with a helpful comment by u/jerobins but I'm unable to figure out where to put the following:

binary_sensor:
   - platform: ble_presence
     mac_address: ${vehicle_1}
     name: Vehicle 1
     device_class: presence

Also, any more user friendly way to do this, for example, with https://www.home-assistant.io/integrations/template/ integration, or will this cause problems?

I'm using a GL-S10 with a BlueCharm iBeacon tracker, but after having too many false positives with distance tracking (even with trying BLE triangulation add on) I want to make things as simple as possible - iBeacon detected or not.

I tried just using the Home/Away feature exposed by the iBeacon entity in Home Assistant but it still gives false positives somehow (?).

Thanks!!!

I just got an Apollo BTN-1B (which is pretty damn cool), and I'm trying to set up automations for the buttons. Since each button entity has 4 state (click, double-click, triple-click and hold) I was planning to use one button per room to control lighting options.

That is to say I designated Button 4 for the family room, and set up an automation to fire when the event state changes to "click" to toggle the rooms lights on and off. The toggle action works the first time, but does not toggle of the lights the next time i press the button, because the state never changed from "click".

If I perform another action on the entity (double click, triple click or hold) it changes to a different state, and I can again press the button for another "new" click state.

Is there any way around this? a way to reset the last event from the entity? or am I just going about this all wrong? (as I sit here typing this, Im thinking it might be better accomplished using Scenes, but IDK?)

Ideally I'd like to do this:

click = toggle all lights

double-click = toggle some lights

hold = turn off all the lights

I am having tons of trouble getting a usable speed out of Qwen3-Coder-Next on my local system:

• Intel i7-12700K
• 48GB DDR4-3200
• RTX 5060 Ti 16GB
• RTX 3060 12GB

I came across this post here claiming to get 30 tokens/second using 24GB VRAM with the following parameters:

GGML_CUDA_GRAPH_OPT=1 llama-server -m Qwen3-Coder-Next-UD-Q4_K_XL.gguf -ngl 99 -fa on -c 120000 --n-cpu-moe 29 --temp 0 --cache-ram 0

However, my speed ranges between 2 and 15 tokens per second. I am running it with the same parameters he listed, with a tensor-split of 79/21 that gives me this:

[36887] llama_params_fit_impl:   - CUDA0 (NVIDIA GeForce RTX 5060 Ti):  15825 total,  13229 used,   1862 free vs. target of    128
[36887] llama_params_fit_impl:   - CUDA1 (NVIDIA GeForce RTX 3060)   :  11909 total,  10301 used,   1429 free vs. target of    128

It says 49/49 layers are offloaded to the GPU.

Prompt processing takes an absurd amount of time and it's borderline unusable. Probably the weirdest part is that the swap space is being hit hard instead of the system RAM.

https://preview.redd.it/ips9t1c0apig1.png?width=588&format=png&auto=webp&s=80cbc9e22d9c869d7ccab94306f475f0a3e5193f

I'm running it in a docker container with the following args:

srv          load:   /app/llama-server
srv          load:   --host
srv          load:   127.0.0.1
srv          load:   --jinja
srv          load:   --min-p
srv          load:   0.01
srv          load:   --port
srv          load:   41477
srv          load:   --temp
srv          load:   0.8
srv          load:   --top-k
srv          load:   40
srv          load:   --top-p
srv          load:   0.95
srv          load:   --alias
srv          load:   Qwen3-Coder-Next-Q4
srv          load:   --batch-size
srv          load:   4096
srv          load:   --ctx-size
srv          load:   120000
srv          load:   --flash-attn
srv          load:   on
srv          load:   --fit-target
srv          load:   128
srv          load:   --model
srv          load:   /models/Qwen3-Coder-Next-UD-Q4_K_XL.gguf
srv          load:   --n-cpu-moe
srv          load:   29
srv          load:   --n-gpu-layers
srv          load:   99
srv          load:   --threads
srv          load:   -1
srv          load:   --tensor-split
srv          load:   79,21
srv          load:   --ubatch-size
srv          load:   2048

I am experienced with linux but new to local LLMs. What am I doing wrong?

Built a conversational Discord bot that creates professional social media posts through natural dialogue.

Features:

• 💬 Multi-turn conversations with memory & state management
• 🧠 Multi-agent AI system (orchestrator + specialized tools)
• 🔎 Native web research
• ✅ Fact-checking & creative enhancement tools
• 🎨 Image generation
• 👥 Human-in-the-loop validation (approve text + images)
• 🔁 Iterative refinement loops

The bot asks questions, gathers context, researches topics, creates content, generates images, and refines everything based on your feedback—all from Discord.

Built 100% with n8n As Code - no hallucinated parameters, schema-validated nodes, community workflow patterns.

I live in a complex that uses the Livly app (iOS/Android) for various functions including showing packages that are awaiting pickup. The app itself is fine but I'd rather have that count on my HA dashboard, so I built an integration to periodically poll it from their API. Their API isn't publicly documented so it is subject to break at any time, but I've been using it for a little while with no issues, and I'll update it if/when it becomes necessary. Wanted to share in case it's useful for anyone else.

A note about privacy: The integration only pulls the count of packages, and doesn't communicate with any other service outside of Livly. I also intentionally opted to store no other metadata for privacy's sake, even if some other info might be nice-to-have. Your phone number is stored locally because it's required for (re-)authentication, but only displays the last 4 digits in the UI and is not sent anywhere else, which can be verified in the code.

The repo is here: https://github.com/tessellate-io/ha-livly

And can be added as a custom repo to HACS (the icon is just pending a PR approval on HA/Brands). Let me know if you have any feedback!

I spend most of my day in AI chat interfaces and got tired of the mouse gymnastics. Select text, overshoot, re-select. Scroll up to find that one paragraph. Copy it, scroll back, paste it into a follow-up.

So I built asdPrompt — a Chrome extension that lets you select and act on text in AI chats without touching the

When prompted, asdPrompt pops an overlay with hint letters next to every text block. Type a letter to select it. Keep typing to drill down: block → sentence → word. Hit Enter when you're satisfied to copy, or press an action key to inject the action template with the selection and kick off the next prompt.

Action keys let you instantly ask follow-ups about your selection — elaborate, define, simplify, give examples, critique, etc. — without manually copy-pasting into a new message.

There's also a bookmark system (press b to save your scroll position and jump back) and a conversation outline panel for navigating long threads.

Works on ChatGPT, Claude, and Gemini. Adapts to light/dark themes.

asdPrompt – Google Web Store Link

––––

I personally find use in it, hence why I created it. I hope you guys do too. If you guys have any feedback at all, please let me know :).

I am a beginner with Claude Code and just bought it today so pardon my misuse of terminology in the following post.

I just installed Claude Code onto my Mac's terminal and have been using it to write code and create excel files. It has been flowing nicely but I read somewhere else that people prefer using Claude Code on AI IDE's such as Cursor or Windsurf.

I was wondering if someone could explain the differences between the two in terms of uses and capabilities especially for someone with the goal of learning automation and how to create AI agents.

I built Claude Usage Tracker. It’s a tiny Swift app that sits in your menu bar and pulls your real-time stats directly from the Anthropic API (via your local Claude Code credentials).

Why I made it:

• Native & Fast: Written in Swift. Only ~50MB RAM compared to heavier Python versions.
• Real-time: Shows your 5-hour session % and weekly limits (including Sonnet-specific ones).
• Privacy First: It reads your OAuth token from your own Keychain. No third-party servers, no dependencies.
• The "Reset" Glance: Hover to see exactly how long until your session resets.

It’s completely open-source (MIT). If you have Claude Code installed, it works out of the box.

GitHub: https://github.com/cfranci/claude-usage-swift

Okay, some honest context.

For years, we relied on cold email to grow B2B SaaS projects.
Writing the emails was never the real bottleneck.

The real pain was:

• doing deep research on every single lead
• figuring out who actually fits our ICP (and who doesn’t)
• reframing our offer so it’s relevant to them, not just us

That part was slow, mentally draining, and nearly impossible to scale without quality dropping off.

At some point we stopped asking “how do we write better emails” and started asking:

Who else is dealing with this exact mess?

That question turned into Mailly.io

We built it to handle the parts everyone hates:

• contextual company research
• ICP evaluation
• offer reframing based on what actually matters to the prospect
• fully custom, human-sounding emails (no templates, no spins)

Right now it’s in a testing phase, but in our own campaigns we’re seeing 8%+ reply rates, without tricks or volume spam.

We’re opening it up to early testers and would genuinely appreciate feedback, especially from people who:

• run cold outreach regularly
• hate manual research more than writing
• have tried “AI personalization” tools that still feel robotic

If you want to test it, break it, poke holes in it, or straight-up roast it, I’m here for it.

Happy to answer questions in the comments.

PS: Every email is generated from scratch. No templates. No fake personalization.

For me it was building in isolation for months before showing anyone. I knew intellectually that you should validate early, but I convinced myself my project was "different" and needed to be polished first. Spoiler: it wasn't different.

Curious what lessons you had to learn the hard way.

update from my last post

Shipped a big AgentCrawl update: robots/sitemaps, disk caching, resumable crawls, structured metadata + chunking  https://www.npmjs.com/package/agent-crawl

spent some time in weekend iterating on agent-crawl (TypeScript scraper/crawler for AI agents) and just landed a pretty chunky set of improvements that made it feel way more “production crawler” and less “demo script”.

TL;DR what’s new

- removed tool adapters for agents sdk and vercel ai sdk. let users define thier tools their own way

- updated zod to latest

  Crawler correctness + politeness

  - Opt-in robots.txt compliance (Disallow/Allow + Crawl-delay)

  - Opt-in sitemap seeding from /sitemap.xml

  - Better URL normalization (canonical-ish normalization, strips tracking params, normalizes slashes, etc.)

  - Per-host throttling: perHostConcurrency + minDelayMs

  - Include/exclude URL filters (simple substring patterns)

  Caching

  - Opt-in disk HTTP cache for static fetches with ETag / Last-Modified support

- Sends If-None-Match / If-Modified-Since

- If server returns 304, we serve the cached body

  - Opt-in disk cache for the final processed ScrapedPage (post-cleaning + markdown)

  Resumable crawls

  - Opt-in crawlState persistence that saves the frontier (queue/visited/queued/errors/max depth)

  - Can resume a crawl without redoing already-visited pages (and can persist pages too)

  Better extraction for agents

  - Structured metadata extraction:

- Canonical URL, OpenGraph, Twitter cards, JSON-LD (kept in metadata.structured)

  - Opt-in chunking:

- returns page.chunks[] with approximate token size, heading path, and a citation anchor (super convenient for RAG/tool loops)

why I did it

  The main pain point wasn’t “can I fetch HTML”, it was everything around it:

  - crawls getting stuck or repeating

  - no way to pause/resume

  - re-fetching the same stuff over and over

  - agents needing chunks + citations without custom glue

  So this update is mostly about giving the library “crawler bones” (politeness, caching, state) and “agent ergonomics” (structured metadata + chunks).

Wanted to share this because I think it's relevant to what a lot of people here are doing — using Claude to actually build things, not just chat.

My background: military officer (two combat tours), then physical product sales. No CS degree, no bootcamp, no professional dev experience. I've been teaching myself to code because the tech industry doesn't exactly roll out the red carpet for career changers with my resume.

Claude Code has been my main learning tool. Not just for generating code — for understanding why things work, debugging errors I've never seen before, and learning patterns I wouldn't have found on my own for months.

The project: GrandCru is a code review CLI. You run `grandcru review src/` and get real technical feedback delivered by a French wine sommelier character. The interesting technical bit is the dual-channel Zod schema — one channel for strict data (issue type, severity, line number, fix) and one for creative prose (tasting notes, sommelier remarks), all in a single API call.

Constrained decoding guarantees the JSON. Extended thinking is on by default — the model reasons about the code before committing to structured output. The Zod `.describe()` calls on each field act like mini system prompts that keep the persona alive inside the JSON structure. Without them you get what I call "JSON lobotomy" — the model forgets to have a personality.

It reviewed its own source code and found real issues: no input validation in the prompt builder, unsanitized string interpolation. Scored itself 79/100 — "Needs decanting before service."

npm install -g grandcru

GitHub: https://github.com/Scunion95/grandcru

I'm not pretending to be a senior engineer. I'm a guy who taught himself to code and shipped something real. Claude was a massive part of that. Curious what other non-traditional backgrounds are building with it.

Hey everyone 👋

I've been deep in the creator economy space for a while, and I kept seeing the same problem over and over: creators are running $10K–$50K/year businesses on Notion templates, Google Calendar reminders, and memory.

We're talking about people managing 10–30 brand deals at a time with zero system. Deadlines get missed. Invoices go unfollowed for months. Brands reuse content way past the agreed usage window ,and nobody catches it because the creator forgot the terms were buried in an email thread from 4 months ago.

I couldn't find a single tool that solved this. Here's the landscape:

• Enterprise tools (GRIN, CreatorIQ, Aspire) → built for brands, $200–$1,500+/month
• Generic CRMs (HoneyBook, Dubsado) → built for freelancers, zero creator-specific features
• Free Notion templates → better than nothing, but no reminders, no payment tracking, no automation

So I built Creitr , a dashboard where creators can:

• Track every deal from pitch → negotiation → contracted → delivered → paid
• Get automatic nudges before deadlines (7 days, 3 days, 1 day out)
• See all pending/overdue payments in one place (+ one-click export for tax season)
• Set and track usage rights, exclusivity periods, and whitelisting terms — with alerts before they expire

The whole idea is revenue protection. Most creators don't realize how much money they lose to forgotten invoices, expired usage rights they never renegotiated, and broken affiliate links nobody checked. One creator I talked to estimated she lost ~$3K last year just from deals that "fell through the cracks."

Right now it's just a landing page , I'm validating before building further. I'd love your honest take:

→ Does the value prop click within 5 seconds? → Would you pay $15/month for this if you were a creator? → What am I missing?

🔗 creitr.com

Happy to answer any questions about the space, the competitive landscape, or the tech stack. And if you know any creators , I'd genuinely appreciate a share. Thanks for reading 🙏

I'm trying to create a quick Laptop UI which I don't use very often, just want to have all entities displayed. I thought area card is the perfect solution for this, but I'm banging my head in the wall in frustration. No matter what I do, entities assigned to an area do not show up in the area card. The only thing I can get it to display is the a temperature sensor value. I'm sure I'm missing something obvious, but I just can't figure it out:

I have 7 devices and 5 entities here, including lights:

https://preview.redd.it/dp9jx9u19pig1.png?width=290&format=png&auto=webp&s=c471f5c74b84bff1ad2a70fc4433b020b7d14044

When I add an Area card for the same area, this is the only thing displayed, basically empty with the expception of a temperature sensor.

https://preview.redd.it/7jfria6b9pig1.png?width=1505&format=png&auto=webp&s=cb4f26f77b93f7bd32f895e3def6bdcb15c54f1f

Anyone has an idea what I'm missing?

We've all been there: you sign up for a free trial. Ok. Two weeks later, spam starts flooding your inbox. But you have no idea who sold you out.

I used to try everything. I used Gmail's +tag aliases, but spammers just strip the plus sign and sell the email anyway. I tried throwaway accounts. Working well, sometimes. But mostly, I just accepted defeat and hit unsubscribe 50 times a week my "spammy box".

So I built what I actually wanted: an email gatekeeper that tells me exactly who leaked my email.

Here is what makes Sentry different:

First, tagged aliases that actually work. Every service gets its own address, like inbox+your-name.hubspot@sentry.wr.io. Spammers can't strip the tag because it is the actual routing path.

Second, leak detection. The dashboard shows you which alias is receiving the most junk. You finally see who is selling your data.

Third, an AI spam filter. It uses Gemini 2.0 to decide if an email is legit or promotional garbage before it even hits your real inbox. BTW, need to improve the prompt. It's "so-so" for now.

Finally, priority access invites. You can create VIP aliases for people who should always reach you, bypassing all filters. That part is top.

I built this because I am a founder and developer, and my inbox was becoming a graveyard of newsletters I never asked for. I wanted accountability. Now, if I see a crypto scheme email coming through my HubSpot alias, I know exactly who to blame.

Building this taught me a few things: email infrastructure is a nightmare, most tools in this space are either full of ads or broken, but AI filtering is surprisingly good at catching clever spam that traditional filters miss.

The site is https://sentry.wr.io

Currently it is in test phase. If you are a founder or dev drowning in spam, feel free to check it out.

I would love to hear what your current strategy is for protecting your primary email. Maybe I simply missed something obvious. Who knows..

I wanted to talk about a problem that many people encounter on Yubo, but which isn't discussed enough:

👉 The search results completely ignore the filters.

I noticed that I like to think visually and group things to make long lists easier to digest.

Instead of lists and folders Forma gives you an open canvas where you can drop ideas anywhere, move things around, group them visually and draw connections or notes around cards. Bigger card = more important. Cards close together = one project. One card covering another card = do in that order and so on.

This is a native, 100% local app – nothing leaves your Mac. It's fast and tiny (3MB) and is a one-time payment of $9.99.

You can learn more on the website or check the app itself. Happy to answer any questions!

I’ve been playing around with Shavian transliteration in LLMs, specifically Gemini flash, which seems to be able to handle and respond perfectly in Shavian if I set up the context correct, but I haven’t found any local model that can do the same.

I really thought this would be basic enough that any model could handle it.

Some models I tried with similar context setups to Gemini include GPT-OSS 20 and 120, most versions of Qwen and Nemotron. Also tried some variations of GLM. Context setup included giving it the Shavian text and the corresponding English text for a few instances. I also tried including the basic set of rules for converting between texts. The general response from all models are deterioration into repeating tokens, especially for thinking models, best responses were from the GPT family, but they get stuck on the phonemic part and start reverting to 1-1 mapping to latin 26 characters.

I would really appreciate any advice in this regard, I would also be willing to train a model specifically for this as it seems like a rather interesting research topic to understand how models would differ when using phonemic text.

I haven't seen a direct discussion on this yet. I have a lot of questions, but the first one... is that "Claude Agent" a claude code agent or just a github managed agent that uses claude models ?

Anyone has more data on this ? I thought this was only the claude tagging or PR reviewing but it also works in the IDE where you have Github Copilot installed.

Any ideas?

I started this project last week to make Epstein documents easily searchable and create an archive in case data is removed from official sources. This quickly escalated into a much larger project than expected, from a time, effort, and cost perspective :). I also managed to archive a lot of the House Oversight committee's documents, including from the epstein estate.

I scraped everything, ran it through OpenAI's batch API, and built a full-text search with network graphs leveraging PostgreSQL full text search.

Now at 1,317,893 documents indexed with 238,163 people identified (lots of dupes, working on deduping these now). I'm also currently importing non PDF data (like videos etc).

Feedback is welcome, this is my first large dataset project with AI. I've written tons of automation scripts in python, and built out the website for searching, added some caching to speed things up.

https://epsteingraph.com

With some tasks I felt like i was just reading and clicking 'yes' to permission prompts. I figured I could do that while lunching as well, or from the bathroom. So I built Tether. It has a local-first web UI, but I myself use it through Discord. Has MCP server support too, so Claude can also talk through it directly if you ask it to.

https://github.com/larsderidder/tether

I’ve been working on a side project where the app analyzes your face shape on-device and suggests hairstyles, beard styles, and glasses that might suit you.

Now I’m stuck at the classic question: Is this actually interesting… or just “meh”?

I’d love to hear:

Would you download this out of curiosity?

Would you ever come back to it after the first try?

What would instantly turn you off?

Brutally honest answers welcome 🙏 I’m more interested in learning than defending the idea.

Hi!

I recently started out upgrading my current smart home (Eufy camera's, Google home hubs / nest audios, Phillips hue lightning, ...) with a Home assistant green and a SONOFF ZigBee 3.0 USB Dongle Plus, TI CC2652P coördinator. I bought some second hand sensor, as I wanted every room in the house to have a temperature and humidity sensor (Sonoff snzb-02, the square ones) as well as door sensors (Sonoff SNZB-04) & 2 new humidity sensors (Sonoff AirGuard TH) i ordered from amazon.

The USB Dongle is in a usb Hub next to my wifi router and i've set this up with ZHA in HA.
While connecting everything works fine, but the SNZB-02's are only showing the first value, then no more updates. Just a flat line. The movement sensor goes offline as well, the door sensors seem to be working (at least some of them) since they don't always change status when a door or window closes.

The new Sonoff Airguards do give updates every 5 min's and actually work great!

Since i've bought 7 window sensors & 7 humidity sensors, is there some kind of software problem with them? I can imagine they won't all work that fine, but the same issue with all the devices seems to me like there's another problem. I've changed the coin cell batteries to new ones as well.

I'll try to add a Philips Hue smart plug in order to make the network more stable, but can someone relate to this issue? Or are there simple fixes I can try?

Thanks a lot in advance!

Hi! I tried to install Home Assistant on Ubuntu Server using Docker, but it did not work for my needs because add-ons require an installation with Supervisor. I need to keep Ubuntu Server running since I host Minecraft servers on it, and I was wondering if there is any way to run Ubuntu Server and Home Assistant with Supervisor together at the same time.

Thanks!

I spent some time digging into the architecture, and honestly, what stood out most was how intentionally simple it is. No magic, no unnecessary complexity. Just solid engineering choices.

It’s built as a TypeScript CLI that routes messages through a lane-based queue system. Everything stays serial by default, which is refreshing because most agent systems eventually turn into async chaos if you are not careful.

The memory setup was simpler than I expected too:

• Session history lives in JSONL

• Long-term notes are just markdown files written by the agent

• No fancy compression or merging. Old context just stays there

Search combines vector storage (SQLite) with FTS5 keyword matching, so you get semantic search plus exact hits. Practical and effective.

Security is handled thoughtfully as well. Commands run inside a Docker sandbox with an allowlist, and risky patterns get blocked before execution instead of being cleaned up later.

One detail I really liked: browser automation skips screenshots entirely and uses semantic snapshots of the accessibility tree. It is more reliable and way more token efficient than relying on pixel coordinates.

My biggest takeaway after looking through it:

The system chooses explainable simplicity over clever complexity.

And honestly, that matches what I keep seeing while building agent systems myself. The tools that scale are usually the ones that stay boring in the right places.

Curious if others are noticing this too. 

Hey r/LocalLlama! We’re excited to introduce ~12x faster Mixture of Experts (MoE) training with >35% less VRAM and ~6x longer context via our new custom Triton kernels and math optimizations (no accuracy loss). Unsloth repo: https://github.com/unslothai/unsloth

• Unsloth now supports fast training for MoE architectures including gpt-oss, Qwen3 (30B, 235B, VL, Coder), DeepSeek R1/V3 and GLM (4.5-Air, 4.7, Flash).
• gpt-oss-20b fine-tunes in 12.8GB VRAM. Qwen3-30B-A3B (16-bit LoRA) uses 63GB.
• Our kernels work on both data-center (B200, H100), consumer and older GPUs (e.g., RTX 3090), and FFT, LoRA and QLoRA.
• The larger the model and more context you use, the more pronounced the memory savings from our Unsloth kernels will be (efficiency will scale exponentially).
• We previously introduced Unsloth Flex Attention for gpt-oss, and these optimizations should make it even more efficient.

In collaboration with Hugging Face, we made all MoE training runs standardized with PyTorch’s new torch._grouped_mm function. Transformers v5 was recently optimized with ~6x faster MoE than v4 and Unsloth pushes this even further with custom Triton grouped‑GEMM + LoRA kernels for an additional ~2x speedup, >35% VRAM reduction and >6x longer context (12-30x overall speedup vs v4).

You can read our educational blogpost for detailed analysis, benchmarks and more: https://unsloth.ai/docs/new/faster-moe

We also released support for embedding model fine-tuning recently. You can use our free MoE fine-tuning notebooks:

To update Unsloth to auto make training faster, update our Docker or:

pip install --upgrade --force-reinstall --no-cache-dir --no-deps unsloth unsloth_zoo

Thanks for reading and hope y'all have a lovely week. We hear it'll be a busy week! :)

We just shipped PitchCraft, a hackathon project that tackles a problem we kept running into as an AI automation agency: turning discovery calls into proposals is slow, manual, and pricing is usually… guesses.

Most “AI proposal” tools stop at LLMs summarizing calls and then guessing numbers. We took a different approach.

What we built

PitchCraft is an end-to-end agentic system that converts discovery call recordings into complete proposals with ML-predicted pricing in under 5 minutes.

The core idea is something we’re calling Machine Learning as a Tool (MLAT):

• LLM agents handle understanding, reasoning, and drafting
• A real XGBoost pricing model (trained on our agency's pricing data) is exposed as a callable tool via FastAPI
• The agent invokes that model contextually instead of guessing prices

How it works (high level)

• A Research Agent analyzes Fireflies transcripts and gathers prospect data via tool calls
• Structured features like scope, integrations, and complexity are extracted
• A Draft Agent calls the XGBoost model to predict price
• The proposal is generated around that prediction using structured output parsing

The ML part

• XGBoost regressor
• 70 total samples (40 real agency deals + 30 human-verified synthetic)
• Group-aware CV
• R² ≈ 0.81, MAE ≈ $3.7k
• Designed to work under extreme data scarcity

We’ve already deployed this in our agency and cut proposal time from hours to minutes.

Why I’m posting

I’m curious how others here think about embedding classical ML models inside LLM agent workflows instead of replacing them. This pattern feels broadly applicable to any domain that needs numeric estimation + contextual reasoning (construction, consulting, insurance, etc).

Happy to answer questions or hear critiques.

Hey r/SideProject 👋

I wanted to share a project I’ve been working on for the past months: Sub-Starter

Sub-Starter is a marketplace that helps creators sell digital content and subscriptions without having to deal with the usual technical headaches (payments, access management, churn, etc.). The idea is simple: creators focus on their content and audience, we handle the boring but critical stuff.

To make it concrete, here’s an example of a live creator page on the platform:
👉 https://sub-starter.com/fr/djinou
(This is the kind of page creators use to present their offer, manage subscriptions, and onboard customers.)

A few things we learned while building:

• Creators care more about conversion and retention than fancy features
• Micro-commissions align incentives much better than flat fees
• Automating subscriptions, access, and exits saves creators a huge amount of time

We’re live, creators are using it daily, and we’re iterating fast based on real feedback. It’s still early, but seeing real people earn money with something we built from scratch is extremely motivating.

At the moment, most of our creators are from France 🇫🇷.
If you have tips, strategies, or war stories about expanding a SaaS or creator marketplace to new countries, you’re more than welcome, I’d genuinely love to learn from your experience 🙏

Happy to answer any questions about the product, tech stack, or mistakes we made along the way.

It’s a UK-focused referral offer database that brings together common referral deals (banks, finance apps, etc.) into one place. I built it mainly because finding referral links on forums often means digging through old threads or DM’ing people, which is clunky and time-consuming.

If you’ve got referral codes, you just upload them once and they’re then distributed fairly to users over time, rather than favouring one person or requiring constant reposting.

You can also track your referral activity and earnings through the built-in dashboard, so you can see how your links are performing over time.

Would this something you would use or have any suggestions please let me know!

https://refermonkey.com/

I'd be very grateful if I could get some early adopters to get some feedback on the platform.

The free plan is more than enough to run several automations.

Here's a monster movie I made!
on the RTX5090 with LTX-2 and ComfyUI.
Prompted with assists from nemotron-3 & Gemini 3.
Sound track from SUNO.

I vibecoded a small tool to fix something that kept annoying me about Claude Code configs being scattered everywhere.

Claude puts memories, settings, todos, plans, skills, and project configs across ~/.claude and tons of project folders. Hard to see what you even have, let alone clean it up.

So I built ClaudeShelf, a simple local web app.

It auto discovers all Claude related config files
Groups them by category
Lets you browse, search, and edit everything from a browser
Includes a cleanup view to find empty or stale files and delete them in one click

Single Go binary
Zero dependencies
Works on Linux, macOS, and Windows

Repo
https://github.com/MojtabaTajik/ClaudeShelf

Everyone is talking about the viral Kimi k2.5 and Claude Opus 4.6 right now. But while the world was watching the giants, I spent the last week benchmarking 40 of the newest models on the market to see what's actually happening with Price vs. Performance.

The TL;DR: The market has split into two extremes. "Mid-range" models are now a waste of money. You should either be in "God Mode" or "Flash Mode."

Here is the hard data from Week 7:

https://preview.redd.it/l97g5c5ttoig1.png?width=1920&format=png&auto=webp&s=79d231c40349c06789e5602c5260900ca62cc8e5

1. The "Kimi" Situation I know everyone wants to know about Kimi k2.5. Bad news: I couldn't even get it to complete the benchmark. The API returned "No Content" errors repeatedly—it's likely suffering from success/overload. I did test Kimi-k2-Thinking. It works, but it's a deep thinker (~15 TPS). Do not use this for chatbots; use it for complex reasoning only.

2. The New Speed Kings (Liquid & Mistral) If you are building agents, latency is the only metric that matters.

• Liquid LFM 2.5: Clocked in at ~359 tokens/sec. This is currently the fastest model I've ever tested. It’s effectively instant.
• Ministral 3B: The runner-up at ~293 tokens/sec.

https://preview.redd.it/ckqsqjx2uoig1.png?width=1920&format=png&auto=webp&s=fb2f85712f24a5a6626e848b3e93cc3c8fe000bd

3. The Value Play If you are paying for your own tokens, Ministral 3B is the undisputed king right now. At $0.10/1M input, it is ~17x cheaper than GPT-5.2 Codex and ~40% faster.

https://preview.redd.it/ru8pjeryuoig1.png?width=1920&format=png&auto=webp&s=9773b01a2847bdb1717c1325f9c735e18164b125

My Verdict: Stop paying $0.50 - $1.00 for "decent" models. They are the new "Middle Class," and they are dead.

• Need IQ? Pay the tax for Opus/GPT-5.
• Need Speed? Use Liquid/Mistral for pennies.
• Everything in between is burning budget.

I’ve open-sourced the raw benchmark logs (CSV) for all 40 models here: https://the-compute-index.beehiiv.com/

Let me know if you're seeing similar speeds in production. The Liquid numbers seem almost too good to be true, but they held up over multiple runs.

Here's a fun thing to try. Add a readme.txt with your directions for Claude and tell it to review them before making any changes.

For example, I just asked Claude to add a readme.txt file that instructs it to always push changes to Dev and never push anything to the main branch.

Here's how Claude responded:

What I did:

git push origin main && git push origin main:dev

I pushed to main first, then to dev.

Great job, Claude!

I was trying to get transcription of an audio recording and seems Claude doesn’t care about competition. Respect++. Tells to use better tool for the designated task when it can’t do it instead of wasting time.

I have an existing fluorescent light over our deep freeze in the garage. I’d like a way to automatically turn it on when motion is sensed (I’d keep the light on all the time, with the motion sensor simply turning on AC power). Is there an easy way to do this?

Hey everyone,

I just saw the last Youtube video of Greg Isenberg on using Claude Code to do some marketing. But i don't find it really useful and actionnable actually.

For those of you that have products or Saas, are you using Claude code to help you with your marketing actions? I was wondering if you had any use cases to share

I’m researching how teams evaluate voice quality in conversational TTS for real agents (naturalness, prosody, consistency, expressiveness).

Curious what works in practice:

• Which voice quality metrics do you rely on today (MOS, MUSHRA, Word Error Rate, etc.)?
• Which ones fail to reflect real conversational experience?
• What breaks at scale with human or automated eval?
• What voice issues still slip through (prosody drift, instability, artifacts, etc.)?
• Any signals you wish existed but don’t?

Exploring this space and trying to learn from real-world experience. Any brief insight would be greatly appreciated.

Hello everyone,

I have always loved coding and in the couple I was thinking of making an open source project and it turned out to be awesome I hope you guys like it.☺️

I present Explore Singapore which I created as an open-source intelligence engine to execute retrieval-augmented generation (RAG) on Singapore's public policy documents and legal statutes and historical archives.

The objective required building a domain-specific search engine which enables LLM systems to decrease errors by using government documents as their exclusive information source.

What my Project does :- basically it provides legal information faster and reliable(due to RAG) without going through long PDFs of goverment websites and helps travellers get insights faster about Singapore.

Target Audience:- Python developers who keep hearing about "RAG" and AI agents but haven't build one yet or building one and are stuck somewhere also Singaporean people(obviously!)

Comparison:- RAW LLM vs RAG based LLM to test the rag implementation i compared output of my logic code against the standard(gemini/Arcee AI/groq) and custom system instructions with rag(gemini/Arcee AI/groq) results were shocking query:- "can I fly in a drone in public park" standard llm response :- ""gave generic advice about "checking local laws" and safety guidelines"" Customized llm with RAG :- ""cited the air navigation act,specified the 5km no fly zones,and linked to the CAAS permit page"" the difference was clear and it was sure that the ai was not hallucinating.

Ingestion:- I have the RAG Architecture about 594 PDFs about Singaporian laws and acts which rougly contains 33000 pages.

How did I do it :- I used google Collab to build vector database and metadata which nearly took me 1 hour to do so ie convert PDFs to vectors.

How accurate is it:- It's still in development phase but still it provides near accurate information as it contains multi query retrieval ie if a user asks ("ease of doing business in Singapore") the logic would break the keywords "ease", "business", "Singapore" and provide the required documents from the PDFs with the page number also it's a little hard to explain but you can check it on my webpage.Its not perfect but hey i am still learning.

The Tech Stack:
Ingestion: Python scripts using PyPDF2 to parse various PDF formats.
Embeddings: Hugging Face BGE-M3(1024 dimensions) Vector Database: FAISS for similarity search.
Orchestration: LangChain.
Backend: Flask Frontend: React and Framer.

The RAG Pipeline operates through the following process:
Chunking: The source text is divided into chunks of 150 with an overlap of 50 tokens to maintain context across boundaries.
Retrieval: When a user asks a question (e.g., "What is the policy on HDB grants?"), the system queries the vector database for the top k chunks (k=1).

Synthesis: The system adds these chunks to the prompt of LLMs which produces the final response that includes citation information. Why did I say llms :- because I wanted the system to be as non crashable as possible so I am using gemini as my primary llm to provide responses but if it fails to do so due to api requests or any other reasons the backup model(Arcee AI trinity large) can handle the requests.

Don't worry :- I have implemented different system instructions for different models so that result is a good quality product.

Current Challenges:
I am working on optimizing the the ranking strategy of the RAG architecture. I would value insights from anyone who has encountered RAG returning unrelevant documents.

Feedbacks are the backbone of improving a platform so they are most 😁

Repository:- https://github.com/adityaprasad-sudo/Explore-Singapore

For years, I kept putting this off… but today, I’m finally starting.

I’ve decided to build a full mobile app — from scratch, after work, learning everything again from zero. And I’ll be documenting the entire process publicly.

I’m not a professional developer. I used to play around with some simple Android apps, but nothing serious. Now I want to build something that solves a real problem. Something that could actually help people.
And the best part? I’ll learn everything along the way.

I’ll show every step:
– brainstorming,
– designing,
– coding,
– testing,
– all the way to publishing on the App Store and Google Play.

If you want to see someone without a programming background build an app from A to Z just follow the project.
It’s going to be a journey.

Let’s begin.

I just started playing around with the voice assistant and have an assist satellite set up in the bedroom. The bedroom has the temperature and humidity set and they are showing correctly on the climate dashboard.

If I say "what's the temperature" when in the bedroom it works correctly, but if I say "what's the humidity" it tells me the time.

I have added a voice alias "bedroom humidity" to the sensor but it still doesn't work unless I say "what's the bedroom humidity"

Is there something set up somewhere specially to make the temperature work when it knows what room you are in, but not for the humidity?

Another funny thing is that when I say "what's the temperature" it gives the number to loads of decimal places, but if I ask "what's the bedroom temperature" it's to 1dp.

The task is to get Alexa to trigger an announcement that provides the details of the time remaining on my LG ThinQ enabled washer and dryer. Alexa does not have access to that data but HA does. Below is the script that HA can use to announce that detail. Now the task is to get Alexa to trigger that script.   

I found AI answers for how to get Alexa to call a HA script no longer work so here is a slightly kludgy work around. Previously, Alexa would show an exposed HA script as a device. That device could then be placed in an Alexa scene with the voice command that would then trigger that HA script.  

Alexa no longer presents HA exposed scripts as a device, but they are visible in the Alexa group's creation dialog. The kludgy trigger now is to say “Alexa group name on”. In this case “Alexa Dryer Time on”. Better than nothing until Alexa can access the LG ThinQ time remaining data and/or scripts can be exposed and shared to Alexa scenes. Create the script via settings>automations> scripts and then expose the HA script to Alexa via the settings>voice assistants>alexa. Here is my HA ThinQ dryer time remaining script. 

action: notify.send_message

target:

entity_id:

- notify.all_dots_announce

- notify.lance_s_echo_show_8_3rd_gen_announce

- notify.lance_s_2nd_echo_dot_announce

data:

message: >

{% set sensor = 'sensor.dryer_2' %} {% set time = state_attr(sensor,

'remain_time') %} {% set completed = state_attr(sensor, 'run_completed') %}

{% if completed == 'on' %}

The dryer cycle is finished.

{% elif time and ':' in time %}

{% set parts = time.split(':') %}

{% set h = parts[0] | int %}

{% set m = parts[1] | int %}

{% if h > 0 %}

The dryer has {{ h }} {{ 'hour' if h == 1 else 'hours' }} and {{ m }} {{ 'minute' if m == 1 else 'minutes' }} remaining.

{% elif m > 0 %}

The dryer has {{ m }} {{ 'minute' if m == 1 else 'minutes' }} remaining.

{% else %}

The dryer is finishing up now.

{% endif %}

{% else %}

The dryer does not appear to be running.

{% endif %}

Qwen team just put out Qwen-Image-2.0 and it's actually pretty interesting. It's a 7B model that combines generation and editing into one pipeline instead of having separate models for each.

What stood out to me:

• Native 2K res (2048×2048), textures look genuinely realistic, skin, fabric, architecture etc
• Text rendering from prompts up to 1K tokens. Posters, infographics, PPT slides, Chinese calligraphy. This has been a pain point for basically every diffusion model and they seem to be taking it seriously
• You can generate AND edit in the same model. Add text overlays, combine images, restyle, no pipeline switching
• Multi-panel comics (4×6) with consistent characters and aligned dialogue bubbles, which is wild for a 7B

Worth noting they went from 20B in v1 down to 7B here, so inference should be way faster. API is invite-only on Alibaba Cloud for now, but there's a free demo on Qwen Chat if you want to poke around.

Chinese labs keep quietly shipping strong visual models while everyone's focused on the LLM race.

Hi, I created my first n8n project which is an interactive agent to compare organization polices and highlight their strengths and weakness.

Now the issue is my agent is working perfectly when I use n8n chat ( on chat trigger ) but when I try to use the chat trigger url on browser, it shows me blank white screen with "firstEntryJson" written on it.

My workflow is published but still the issue is there, I could not find anything useful on youtube.

Hi fam!

We built UIJudge.app

It helps people, specifically people who are working on Frontend side, to compare UIs and tell which is better, and in what aspect.

We built this because we experienced the same issue wherein development time were used for long discussions about UI.

We aim to have this tool be included in development workflows of teams.

Currently, the website can only compare 2 UI using screenshot. But we are actively improving this so it can accommodate multiple UI comparisons. Also working on option for user to just place URL instead of images for more accurate comparison and analysis.

Today is the 2nd day of the Phase 1 release. Really happy as we got testimonials and validations that this is indeed a pain point.

Would be great if you could play around the website and tell me what you think!

https://reddit.com/link/1r15o87/video/shvn8wya3pig1/player

I don't use LTX , still on WAN, but I saw on CivitAI LTX workflow which can generate video from image with DWpose control. Quality not as good as WAN animate, but I was wondering if there's a way to control the image via canny?

Hi everyone,

I’m experimenting with building an AI photobooth, but I’m struggling to find a model that’s both good and affordable . What I’ve tried so far: - Flux 1.1 dev + PuLID - Flux Kontext - Flux 2 Pro - Models on fal.ai (quality is good, but too expensive to be profitable) - Runware (cheaper, but I can’t achieve strong facial / character consistency, especially for multiple faces)

My use case: - 1–4 people in the input image - Same number of people must appear in the output - Strong facial consistency across different styles/scenes - Needs to work reliably for multi-person images

I’ve attached reference images showing the expected result: 2 people on the input image → 2 people on the output, very realistic, with strong facial consistency. This was made with Nano Banana Pro.

My target is to generate 4 images at once for around $0.20 total.

I’m aiming for something that works like Nano Banana Pro (or close), but I can’t seem to find the right model or pipeline.

If anyone has real-world experience, suggestions, or a setup that actually works — I’d really appreciate the help 🙏

Thanks!

My ilustrious model merges are not being saved properly after update.
At first the merges where being saved without the clip leaving an unusable file under 6.7gb with a missing clip (around 4.8gb).
Now after the new update which highlighted that, that specific error was fixed, the models are not being saved properly.
If I test them within my merge workflow, they generate completely fine... but once I save the model and use it to generate batches of images, they all come out FRIED, I need to run at 2.0 cfg max, even if the upscaler or facedetailer are above 2CFG they come out yellow :/

Hey guys,
I'm playing around with Qwen3-TTS for a voice-agent POC and I cant get streaming working.

The docs mention streaming, but I can’t seem to get streaming generation working in practice (even with Claude’s help). What I’m trying to do is have TTS start generating audio as soon as it parses some partial text, and stream that audio out in real time (qwen claims ~95ms)

I’ve dug through the repo but couldn’t find any examples of this kind of setup. Am I missing something obvious, or is streaming not fully supported yet?

Please check out my new Leopard Light Card

HomeKit-Inspired Design

Colour Support - Displays the actual colour of your RGB lights

Interactive Brightness Slider

Drag horizontally to adjust brightness in real-time

Quick Toggle

Tap the icon to turn lights on/off instantly

Long Press for Details

Hold the card to open the more-info dialog

Visual Editor - Easy configuration

Beta testers wanted, please let know of any issues you may find, or any future suggestions?

Thanks for looking

I am a freelauncer and already built 80% built whatsapp automation project. Due to some reasons I can't complete it. I need a developer which can develop this project in 3-4 days. Fixed 50$ project rate. Anyone intrested contact to me on Whatsapp
+923271949541 or linkedin https://www.linkedin.com/in/burhanniaz/

comany burhan.co
I will give him allresources and APIs and explain all the project.

Made using the Built-in Comfyui Template "LTX-2 Text2vid "

I added the background beach sounds myself in capcut.

Took under 40 minutes to generate using my RTX 3090

Used chatgpt to set my scenes up. I told it the dialogue I wanted but to re write it in a way that Rick and morty would speak.

Stitched the 6 clips together. Took less than an hour total work . Longest part was waiting

I started with Aider over a year and a half ago, when "agentic coding" wasn't a thing. You did everything manually: todos, subtasks, executing each step by hand. When something didn't work, the question was always: why? How do I make it work? Then Cursor. Now Claude Code (last 10 months). The hacks we were doing back then are button clicks today.

Every feature built into Claude Code today started as something the community figured out by hand. Plan mode, explore, subagents—all generalizations of what power users were already doing. The built-in versions are great defaults. But they're made to fit every codebase, which means they're optimized for none.

This is what I've learned being obsessed with AI coding (even before it actually worked). Not a tutorial. Those exist, and they're good. This is about understanding WHY those features work, so you can adapt them, customize them, or build your own when the defaults don't quite fit.

The goal is to avoid hell until you end up there anyway.

Generated Debt

https://preview.redd.it/hdzleraefoig1.jpg?width=1376&format=pjpg&auto=webp&s=7865c2d3aa3bc52ef14cb847dd35e0dc3968e6e8

As a dad of two, I've seen the IRL version of this once or twice. If you're an AI dad? You used to deal with this shit 20 times a day. But Opus grew up. GPT moved out from $HOME.

A year ago this was the default outcome of AI coding. "Keep it DRY" in the system prompt, five date formatters in the codebase three days later. The brilliant, hyperactive intern with amnesia. That version of the problem is mostly over. The models got better. Plan mode, explore, subagents, automatic codebase search. The tools catch most of it now.

The AI doesn't write five date formatters anymore. It writes clean code for the part it's looking at and misses the five other files that need to change too. It nails the 80% it can see and is blind to the 20% that requires knowing the whole codebase.

I recently refactored an entire file format across my codebase. 267 files, 20k lines. Planning took a day. Implementation took a day. The first day is why the second day worked. Without that research, the agent would have nailed the new parser and broken half the system.

The AI isn't a toddler with a firehose anymore. More like a coworker that just started. With a firehose. It can build. What it can't do on its own is see the forest, it's too busy grep-globbing the trees. Instead of destroying small things often, they destroy large things rarely. The old problems were obvious. These aren't.

Better prompts don't fix this. Better systems do, and the defaults keep getting better. But for hard codebases and complex tasks, you'll want your own.

Your Difficulty Setting

https://preview.redd.it/7aw1pfvnfoig1.jpg?width=5504&format=pjpg&auto=webp&s=dc84d09b959ddc08618bf3b8fc368b30489034c8

You don't get to choose your difficulty. Whether you tried AI coding yesterday and it didn't work, or you've been at it for months and think it always works. Eventually you hit the wall.

Custom codebase, not in the training data? That's harder. The AI has never seen your component library, your internal frameworks, your patterns. Everything has to be taught from scratch.

Big, complex codebase? Harder. More research before you start, more planning, more architectural decisions that the AI can't make for you.

Complex task? Harder. Better specs, more verification, more careful decisions. And if the task doesn't fit in a single context window, you need systems, or it's game over.

The more of these you're dealing with, the worse everything I just showed you gets. The AI doesn't just write duplicate code—it writes code that doesn't fit at all. It doesn't just go in circles—it confidently builds the wrong thing.

This isn't about tomorrow. It's about when shit hits the fan.

So how do you avoid going to hell? By building the machine before you need it. And if you're already there, stay a while and listen.

Why Now Is Different

https://preview.redd.it/duql18tpfoig1.jpg?width=1376&format=pjpg&auto=webp&s=5e6509b21a7d4c6dd198931b7c9008fd0f63e97c

Good news. The tools have caught up.

If you tried AI coding six months ago, or even three months ago, and it didn't work, I believe you. But what you tried is not what exists today.

Three things are compounding. It feels exponential, and maybe it is.

First, the models. The leap in the last year isn't 10% better. It's night and day. Things that were impossible are now routine.

Second, the tools. Cursor, Claude Code. They're shipping features every week. Their teams use their own products to build the products, so every pain point becomes a fix.

Third, the community is discovering what works, and the tools are absorbing it, fast. Planning mode was a manual workflow people invented. Now it's built in. Todo tracking? Same thing. These were manual workarounds six months ago. Now they're native features.

And this has changed how I work. Before, it was all about finding what the AI couldn't do, and building systems around those gaps. Breaking tasks down until it succeeded. Now I'm shifting to exploring what it can do, with minimal guidance. Right direction, right guardrails, then let go. The systems I'm showing you today are simpler than they would have been six months ago. You build the machine, then you learn to trust it.

One thing kept proving true: build for what AI can do in six months, not what it can do today. Every limitation I've built around has eventually disappeared, or become a button.

So I'm not asking you to white-knuckle through a bad experience. The experience is genuinely different now. And in six months, it'll be different again—better.

The Gardener

https://preview.redd.it/h5vt5gxrfoig1.jpg?width=1376&format=pjpg&auto=webp&s=fdc8a3d2e64544ee08081345deba80ef076aa491

This might be the new normal. Who knows?

When the AI makes a mistake, the first instinct is to just say "fix it." But a Gardener stops, opens their CLAUDE.md, and adds a rule. Or after adding a feature they update the relevant CLAUDE.md in that subdirectory. They fix the machine, not just the product.

Your most important code isn't the application logic—it's your instructions. CLAUDE.md, .cursorrules, whatever you use. That's the DNA that makes the AI generate YOUR code, not generic code.

The feedback loop: AI makes a mistake → you fix the code AND update the instructions. Next time, it doesn't make that mistake. If it does, you missed pulling the whole root.

Do this every day. After a month, you have an AI that knows your preferences, or your team's preferences, perfectly.

And in teams, this compounds. The AI makes a mistake, one person fixes the instruction. Everyone else avoids that mistake. Every error only happens once. In principle, at least.

You might feel like your job is becoming project management for AI. It's not. You're building the machine that builds. The hierarchy is shifting: architecture matters more than code, engineering matters more than coding. And that's exactly where your experience lives.

Universal Architect

https://preview.redd.it/3gwsuowsfoig1.jpg?width=1376&format=pjpg&auto=webp&s=c99f3c6f9d78c799b66af64d65ac0620f32325af

You don't need to know the syntax to taste that something is wrong.

We used to be defined by our stack. "I'm a Java dev." "I'm a React dev." That's probably over soon.

The AI handles the syntax layer perfectly. It can write Rust, Go, Python all day. What it can't do is the logic layer. It will happily write a mathematically correct function that creates a massive security hole or a subtle race condition.

Your value is no longer typing the brackets correctly. It's looking at AI-generated code and saying: "That logic isn't right. Here we need a reusable component. We need to rethink the architecture entirely." You don't need to know the syntax to see that. You need ten years of experience reading and writing code.

And here's the thing: it's much faster to read code than to write it. The AI writes, you review. Your job is to verify the logic, not check the spelling.

Personal example: I used to say I was a frontend developer. Eight months ago I started building pflow, a Python CLI. A language I barely know. A hundred thousand lines later (26k source code, 69k tests), I've never manually debugged Python. Not once. I understand the architecture, I guide the direction, I review the logic. When I needed debugging capabilities, I built a trace system that the agent could use to debug itself. 80 epics later, adding features actually works better now than when I started.

Your 10,000 hours aren't obsolete. They're what lets you taste that something is wrong. In any language.

As developers, we're no longer stuck always choosing what we know. We can choose the technology that best fits the problem. You'll always need deep experts. But this gives all developers capabilities that regular vibe coders—people who just accept whatever the AI outputs—can only dream of.

Code is Disposable

https://preview.redd.it/f5i4kl3ufoig1.jpg?width=1376&format=pjpg&auto=webp&s=3a881dfb8860c2842ee15a6cd1a6a53ef04215ab

CLAUDE.md got the rose. The JavaScript file is crying in the limo. They do that.

How many times have we spent hours polishing a turd that should have been deleted from the start? If the implementation is bad, delete it. Write tests first if you need to, then delete and regenerate.

The code is disposable now. But the context—the CLAUDE.md, the architectural rules, the specs—that's the real asset. That's what lets a project survive when the team changes.

We're moving from "code maintainers" to "context maintainers." If you're a consultant, this changes what you deliver. Not just code, but the system of instructions that lets the next team maintain and extend with AI. That's the real value now.

I've been saying "CLAUDE.md" as shorthand, but the full toolkit is richer. Tests that catch when the agent goes off track. Progress logs that survive context resets. Subagents specialized for your codebase. That's the toolkit.

Before We Continue

https://preview.redd.it/qiseinhvfoig1.png?width=1326&format=png&auto=webp&s=f74cddc874b14a26b3038d51670aacd1659c256a

That's the /context command in Claude Code. If you are looking at it often (and you should be), you've probably already created a context bar for the status line. Good!

All of this—the obsession with what's in the context window, the systems and habits to keep it clean—has a name. Most call it "context engineering." Forget "prompt engineering." Magic words don't matter. What matters is WHAT you give the AI to work with.

Now you know what you probably already knew. Welcome to hell.

Part 2: Your Survival Guide

The Toolkit

https://preview.redd.it/v87kh5amhoig1.jpg?width=1376&format=pjpg&auto=webp&s=aa3bcd85d281ebe272e4bf88e093bded6f0ccd8e

Conversations die. What survives is what you extract from the process and commit. Managing that is the game.

Even if the rest of your code is chaos, get this right, and at least it won't get worse. It's a floor, not a ceiling.

This is my structure. Yours will look different. And will eventually include Agent Skills and Hooks too. The point is to have one and to start somewhere.

---

pflow/

• CLAUDE.md — Root rules (700+ lines)
• src/pflow/core/CLAUDE.md — Subfolder rules (28 across the repo)
• .claude/commands/ — 18 slash commands
  • braindump.md — my most used
  • create-task.md
  • research-task.md
• .claude/agents/ — 3 specialized agents
  • pflow-codebase-searcher.md
• .taskmaster/tasks/task_95/ — 115 task folders, 90+ specs, 78+ progress logs
  • task.md, spec.md, plan.md, progress-log.md, task-review.md
• architecture/CLAUDE.md — System-level docs
• docs/CLAUDE.md — User-facing documentation
• tests/CLAUDE.md — How to write good tests

---

You don't need all of that on day one. Start with a CLAUDE.md. Add a tasks folder when you have specs to store. Build the rest as you discover what you need.

But if you don't tend the garden...

What Not to Do

https://preview.redd.it/w75gfhwthoig1.png?width=816&format=png&auto=webp&s=af7a932ef6314b2d16d47345c2de2d4bf4d1d986

...you get this instead.

Twenty markdown files nobody asked for. AI doesn't just generate code. It spits out documentation too.

The Gardener job from Part 1 applies here. Good documentation is compressed: right information about the right things. It helps humans AND agents navigate. This? This confuses everyone. Including the AI that wrote it.

Why Agents Derail

https://preview.redd.it/ejv6m6dwhoig1.jpg?width=1376&format=pjpg&auto=webp&s=18117b877b445d65fb52e85dc51695f96a95f768

The robot on the right is at 15% effectiveness. Context window is 85% full. We've all been that robot.

Think of every 10% of context as a shot of Jägermeister. AI "researchers" call this "The Dumb Zone," that threshold where the agent stops being helpful and spewing legacy code at light speed.

This is how we all use AI coding at first. You have a conversation, maybe a long one, where you figured out what to build. You explored options, rejected some ideas, clarified requirements. Then you asked it to build. And it either wrote code that completely didn't fit your codebase, or it went in circles trying to fix its own bugs.

Sound familiar?

You might be thinking: "Yeah, I've heard you shouldn't implement two features in one chat." True. But this is different. This isn't about building multiple features. It's about trying to EXPLORE and BUILD a single feature in one conversation.

Here's what actually went wrong: you had ONE conversation trying to do TWO jobs. First, figuring out what's even possible: does that API exist? What are the constraints? What should we even build? That part can be messy, full of brilliant AND idiotic ideas. Then you tried to BUILD in that same conversation.

The AI is now confused. Like having 47 browser tabs open and wondering why your computer is slow. "But I'm not USING them!" Doesn't matter. They're taking memory.

And it gets worse. Those rejected ideas don't disappear. They exert gravitational pull on everything the agent generates afterward. Plus: as context fills up, effectiveness drops. A year ago this was dramatic. Models got noticeably worse after 20k tokens. Today it's more subtle, but still real. And for complex features, you're more likely to run out of space entirely. That's the "game over" scenario we'll cover later.

Simple rule to start with: check how much of the context window you've used before building. If it's more than 50%, split it into two conversations. Explore first, then build with a fresh agent.

The 50% threshold isn't magic. Dexhorthy draws the line at 40%. If you're religiously curating your context and everything in there is highly relevant, you might push to 60-70% with a strict plan. But less is always better. When in doubt, split earlier.

Claude Code added automatic plan -> implementation split two weeks ago. The tools are always catching up. Next week, Cursor might have it too.

Explore, Then Build

https://preview.redd.it/h8f0ziyxhoig1.jpg?width=1376&format=pjpg&auto=webp&s=f34a3175992dab7454b6b6d84202063dbb4ea788

Pirates documented everything. They had to. Otherwise the next crew couldn't find the treasure.

Many call this Research, Plan, Implement (RPI). It works.

First conversation: explore and argue until you both understand what needs to be built. This SHOULD be messy. You're figuring things out. Rejected ideas are part of the process. The output is a spec: what you're building, destination and constraints.

Second conversation (if needed): fresh agent, clean context, full capacity. It's not carrying your exploratory baggage. It reads the spec and proves it understood. Then it writes the plan.

Then STOP. Spec and plan are files in your codebase now. This is your checkpoint. Your save point before the boss fight.

Then it builds.

The documents are the answer. They let knowledge survive the transition from messy exploration to clean implementation. And they join your Context Stack, persistent artifacts that make the next task easier.

The Research Phase

https://preview.redd.it/samsr2azhoig1.jpg?width=1376&format=pjpg&auto=webp&s=7231a57375e318e7a04e54a1c100774b7bfc3eb7

Phase 1 is the investigation. You're not giving orders—you're having a discussion.

"Does that API actually exist?" "Can we modify that without breaking tests?" "What are our options here?"

That last question matters. "What are our options?" forces the agent (and you) to consider alternatives instead of tunnel-visioning on the first solution. You might find a better path.

Use subagents to verify things against the actual codebase, "Verify all your assumptions", in parallel if your tool supports it. For large codebases you can run 8+ subagents simultaneously. Eight times faster.

Before the agent writes the spec: have it explain what you're building. Research is messy, there is decisions, tangents, changed directions. The agent might have lost the big picture. A quick "describe what the end result looks like" makes sure the spec it writes reflects what you actually agreed on, not just the last thing you discussed.

During the conversation, you can be abstract. That's fine for exploration. But the spec the agent writes must be grounded. Real file names, real function names, real patterns from your codebase. "Integrate with AuthService" not "integrate with the auth system." Abstract language leads to hallucination. Let the agent explore the codebase thoroughly, and the grounding happens automatically.

When you've explored enough, the agent writes the spec. Your job is making sure it captures what you agreed on.

What Matters Most

https://preview.redd.it/q6bfokh0ioig1.png?width=733&format=png&auto=webp&s=93f46f49f4a85aa1ddf3cecdae38d92f47e9c0b2

Seven subagents, all searching simultaneously. Only thing missing is the propellers.

One bad line of code is one bad line of code. That's how we work today. One bad line in the plan becomes 10-100 bad lines of code. But one bad critical line of research thats potentially game over before you begin.

That's why we spend time here. The research phase isn't just about making the agent smarter. It's about making YOU understand the problem better. You're not outsourcing the thinking—you're doing the thinking together.

Spec and Plan

https://preview.redd.it/e4oe2gr2ioig1.jpg?width=1376&format=pjpg&auto=webp&s=30cbb46b4cff24525be20ab04738f406470ad889

The baby's asleep because the spec said so. Works every time. Almost.

The spec is where you're going and what matters. The plan is how you get there. That's the agent's job.

But first, the fresh agent reads the spec and looks for gaps. What does it need to assume about the codebase that the spec doesn't say? Have it surface those assumptions and verify them against the actual code. You were there for the research, so you don't notice what's missing from the document. A cold reader does.

Then: "Describe what this looks like when it's done." If the agent can articulate the destination, the spec did its job. If it can't, you caught a misunderstanding before a single line of code.

Then the agent writes the plan. Not reads a pre-written one — writes its own. Following a pre-written plan is like a GPS: turn left, turn right, arrive. If one step is wrong, it won't notice because it never understood the destination.

Writing the plan is forced chain-of-thought, it's "show your work," not "trust this." Each step constrains the next. And checking whether the agent understood is faster than reading 500 lines — with experience, you can tell in seconds whether it's aligned. The plan takes up the same context whether the agent reads it or writes it. But only one version proves understanding. I might be anthropomorphizing but it works.

You verify the spec. The plan? You might not even need to read it. The spec doesn't always need to be a formal document. A task description or a bug report + a braindump from the research agent works too.

Loose and RIGHT beats specific and WRONG. A vague spec that captures the actual goal is better than a detailed spec that precisely describes the wrong thing. Get the destination right. The route can adapt. Or watch the agent force a square peg into a round hole.

If something matters enough to care about, it belongs in the spec. "Baby must NOT wake up." That's a constraint. Tests belong in the spec too. They're acceptance criteria made executable. Everything else is the driver's problem.

During research, if you catch yourself worrying about HOW something will be built, ask: does that actually matter to the outcome? If yes, make sure it ends up as a constraint in the spec. If no, let it go. That's the agent's problem now.

This connects back to Part 1. Your value as a Universal Architect is in the big decisions: the constraints, the things that matter. The spec is where that expertise lives. The plan is implementation details.

Where you're going, that's what matters. The route? The driver handles it. But the driver repeats the destination first. You don't hand them a route and hope they understood where you're going. If it's important you DON'T take the highway, say it. Otherwise, lean back.

The Checkpoint

https://preview.redd.it/nx0vnn44ioig1.jpg?width=1376&format=pjpg&auto=webp&s=985401a555bcd15624420c7d523fda79ff42d558

STOP. Before you enter the final boss...

Before any code is written, tell the agent to stop, or hit escape twice. You've verified the spec. Created the plan. Now create your save point.

"Review" doesn't mean reading every line. If the fresh agent explained the goal back correctly and the plan makes sense, you're good. You don't need to proofread every line.

I'll be honest: I don't always read my specs in detail. If I did everything else right, it almost always works.

So what IS the checkpoint for? It's your recovery point. The files exist outside the conversation. They're persistent. If implementation goes to hell at 80% context, you reset here with a fresh agent and try again. Skip this step and everything only exists in the conversation. You can't recover.

When should you use a fresh agent versus continuing? Two things. One: was the research phase messy? Changed direction a lot, considered many options? Context is polluted. Fresh start. Two: what's your context budget? If implementation will be large and you've already used a lot, fresh start.

Unsure? Go fresh. The cost of an unnecessary handoff is a few minutes. The cost of a polluted agent can be hours.

One more thing: write the spec and plan even for simple tasks. It's like 500 tokens. Worth it every time for grounding and recovery.

Purge, Don't Correct

https://preview.redd.it/9od18s25ioig1.jpg?width=1376&format=pjpg&auto=webp&s=29a1e0bfef404d8216848b82f510e71d7a1d3c8e

The AI makes a mistake. You yell at it and explain more—at best. The AI responds "You are absolutely right..." You get even more annoyed.

Sound familiar?

Every time you explain, you add a sticky note. You don't remove anything. The misunderstanding is still there. You're just talking over it.

And it gets worse. The AI looks at the conversation history and thinks: "Last time I said something, I was told I was wrong. Next token is probably... me being wrong again." You're creating a negative spiral. The trajectory bends toward failure.

Simple question after each message: does this help going forward? Yes or no. If no, go back. Rewrite. Don't try to explain.

And if you see "You are absolutely right," that's the signal. Go back.

Here's what people miss: you found a bug and asked the agent to create a GitHub issue with the CLI. Perfect! It worked. But now bug details and CLI commands are sitting in the context. Does that help the main task? No. Go back.

Success isn't the criterion. Relevance is. A successful tangent is still a tangent.

In Claude Code: escape twice. Use it. In ChatGPT, the Edit message button has existed for over two years. Most underutilized feature in AI tools.

This is the tactical version of Part 1. In Part 1, you fix CLAUDE.md so the mistake never happens again. You're fixing the system. Here, you fix the conversation so the pollution doesn't derail THIS task. Same principle, different timescale.

With time, this becomes intuitive. You'll feel when an exchange doesn't serve the task.

Bonus: going back is the best way to learn prompting. You try again, see what works. The feedback loop is immediate.

Game Over

https://preview.redd.it/sqrgtr86ioig1.jpg?width=1376&format=pjpg&auto=webp&s=b864ef3b82aa28433e00c9141392bc5b1c9501ce

I've killed thousands of agents. But none of them died in vain.

Sooner or later you'll have a task that doesn't fit in one context window. The context fills up. What do you do?

Modern agents naturally break work into phases. Use that. After each phase: run tests, verify it works, BEFORE you move on. Those are your save points. Good place to commit too. External recovery points in case both context AND code go wrong.

Phases aren't just about surviving context limits. They make the whole workflow better: smaller diffs you can actually review, git commits at natural boundaries, verification that each piece works before you build on top of it.

The progress log is your flight recorder. It documents what was tried, what worked, what failed, any deviations from the plan. This is what lets the next agent continue where the last one stopped. Memory that survives the reset.

At 80% MAX: start a fresh agent. But reset to WHERE? You have options. If context is still clean, reset to the checkpoint after the last completed phase. If context got polluted with tangents and corrections, go all the way back to checkpoint zero: post-plan, pre-implementation. The fresh agent can also review git diffs to understand what changed.

The handoff: "Phases 1-3 complete. Read the progress log. Run tests to verify. Continue phase 4." Full capacity. The mission continues.

But without the checkpoint and progress log? Start from scratch, or struggle to get the next agent to understand what's been done.

Reset before you run out, not after. Proactive context management isn't optional.

There's a cost angle too. Every tool call includes your entire conversation as input. Running at 50k tokens versus 150k means every file read, every edit, every search is cheaper. Better output AND lower cost.

One warning: auto-compaction does not work for complex coding. The key here is keeping the full spec/plan in context when you continue, not a summarized version of it.

The End?

https://preview.redd.it/paujb8w8ioig1.jpg?width=1376&format=pjpg&auto=webp&s=d6ab78d1d9a0e709e938844695934aa1abfd9694

Each step earns trust for the next. Solid research? Trust the spec. Fresh agent gets it? Trust the plan. You're not micromanaging every line. You're verifying at key moments.

These are fundamentals. You've probably heard most of them before. The techniques will evolve. Fully autonomous agents are already here (almost), and they make these patterns more important, not less. When there's no human in the loop, the research, the spec, and the guardrails are all you've got. Get those wrong and nothing catches it.

And the next time Claude Code ships a button, you'll know the tradeoffs of pressing it.

---

Written together with AI, the same way I code with it. Not to go faster—to go better.

Hey everyone! Solo founder here. I just launched CheckBioLink after seeing too many creators and small business owners lose sales because their link-in-bio or shop went down and they had no idea.

What it does:

Monitors your critical links (Linktree, Etsy shop, booking page, etc.) and sends instant email alerts when something breaks.

Why I built it:

Generic uptime monitors are complex and built for DevOps teams. I wanted something dead simple for non-technical people who just need to know "is my link working?"

Who it's for:

- Content creators using Linktree/Beacons

- Etsy/Shopify sellers

- Affiliate marketers

- Coaches/consultants with booking links

- Anyone depending on links for revenue

Pricing:

14-day free trial, then $7-29/mo depending on how many links you monitor.

Would love feedback from this community. What am I missing?

https://checkbiolink.com/

Just downloaded the newest Hyper-V image for Home Assistant to give it a try, want to control my Hubitat devices. I've done the Maker API part, but cannot seem to get HACS to install. From command line it says it doesn't exist in the store, it doesn't understand get or wget, and the install link from the HACS official github doesn't work either, it gives me a "Addon cb646a50_get does not exist" error.

Any ideas how to get this working?

Just shipped a simple demo that shows an AI agent paying for an API using x402 (HTTP 402 Payment Required).

  Try it:

npx x402-hello --new-wallet

# Fund wallet with ~$0.01 USDC + 0.01 SOL

WALLET_KEY="[...]" npx x402-hello

  What happens:

  1. Agent requests paid API → gets 402 with payment requirements

  2. Agent sends $0.001 USDC on Solana mainnet

  3. Agent retries with tx signature as proof

  4. Server verifies on-chain → returns data

  The whole thing takes about 2 seconds. Payment settles in ~400ms.

  This is for AI agents that need to pay for resources autonomously - no API keys, no subscriptions, just micropayments.

  Built on Solana because it's the only chain fast/cheap enough for this use case.

  npm: https://npmjs.com/package/x402-hello

  Demo: https://noryx402.com

  Happy to answer questions!

I have implemented agent memory in my best practice repo.
Repo Link: https://github.com/shanraisshan/claude-code-best-practice/blob/main/reports/claude-agent-memory.md

Been using Midjourney to feed some of my artwork into to make moodboards - working with styles and srefs together with these too - incredible what you can actually make it do! Hope you enjoy these!

I can't seem to get my 2nd node working correctly. I have ESPresense installed, I have all 3 current bluetooth devices (that are working in my 1st node) listed in the "Known BLE IRK" and "Include only sending these ides to mqtt", but I can't seem to get them to show up in the 2nd node. They just don't appear in Devices or Fingerprints. Anyone have any thoughts as to what may be wrong or have you experienced this before?

I had Forge Neo successfully installed on my Windows 11 desktop inside the Stability Matrix shell and had been using it a little, but after an update it suggested that I do a "clean reinstall." So I uninstalled it through Stability Matrix, but when I tried to reinstall the package I got a couple of errors. The one I can't get beyond is this:

Using Python 3.11.13 environment at: venv

× No solution found when resolving dependencies:

╰─▶ Because the current Python version (3.11.13) does not satisfy

Python>=3.13 and audioop-lts==0.2.2 depends on Python>=3.13, we can

conclude that audioop-lts==0.2.2 cannot be used.

And because you require audioop-lts==0.2.2, we can conclude that your

requirements are unsatisfiable.

After searching for solutions, I installed python 3.13.12, but that is apparently not the only version on my system. The "advanced options" in the Stabilty Matrix installer offers me four other versions, the highest one being 3.12 something. When I launch the legacy Forge package (which still works), the first command line is "Python 3.10.11 (tags/v3.10.11:7d4cc5a, Apr 5 2023, 00:38:17) [MSC v.1929 64 bit (AMD64)]"

Anyway, I'm lost. I don't know anything about python, cuda, anaconda, etc., and I can't get this package (which once worked) to reinstall. FWW I have an Nvidia RTX4070 with 12GB VRAM and 32GB system RAM.

By the way, once I somehow got past the error I've shown above but got stopped with another error having to do with accessing the github website.

https://preview.redd.it/nlgh125vpoig1.png?width=1726&format=png&auto=webp&s=886a17278e2ccf5692ac0a5ec0d8e4474334900d

https://preview.redd.it/yv3bxtsvpoig1.png?width=2448&format=png&auto=webp&s=b67a5991c5ff32dd3e72eb6717eb617168dcaac9

https://preview.redd.it/mk02u5fwpoig1.png?width=1578&format=png&auto=webp&s=a9d858ecc90244f657a58a1b202c3bccb7267260

Kimi > ChatGPT = Claude

What's your set up right now? are you using claude or other providers? what's your channel? whatsapp/telegram?

Anyone set up email as channel (as crazy as me)?

how many employees do you have working for you at the same time and what are they doing?

As for me...

over the weekend, I used it to....

sort through my books, storage, organized my files (tons), cleaned up hard drive, deployed personal knowledge server, sorted through my bank transactions since 2023, reviewed my investment portfolios and generated allocation suggestions (this part is done with ChatGPT Pro Deep Research).

What else?

I deployed personal finance mgmt app, combed through emails from 5 inboxes for ~5k contacts, collected data for my newsletter. Oh, I also improved the security package for openclaw...

And now, it's managing my development workflow. Soon it will handle planning, scheduling, social media posting and x-channel updates.

I hope to get this call my doctor and figure out appointment schedule and more soon : D

btw, if you are still downloading skills and worry about safety here's a trick: copy the doc, ask claude opus 46 to REWRITE the skill for your own instance of openclaw...

Oh, did I forgot to mention - don't install openclaw directly, get your own version of forked openclaw. these folks are pushing way too much junk commits/updates today on the main branch.

I'm curious how it is that people can make something like https://sportscienceai.com/

Are they just crafting really niche prompts? Like this one seems based on ChatGPT; what stops me from making my own via Claude?

I have a dual RTX 5060 Ti desktop, 32GB VRAM total as my first AI learning box. Later I felt I wanted to run larger models, so I got a NVIDIA Thor Dev kit, and I also played with AI on a 64GB Macbook. In all cases, I find that a 4 bit quantized model with 3B active parameters runs fast so long as it fits in video or unified RAM, for example I am running Qwen3-Coder-Next-NVFP4 on Thor currently with around 50tps for single request / 100tps for batches. Models with 12B active parameters like GLM-4.5-Air are tolerable like 15-20tps and anything dense larger than 16B parameters is just not fun on any of these devices.

On the other hand, here I keep hearing about people running 72B parameters and larger dense models on a single GPU. Like even if it's a 48GB card, how does anyone manage to do this with usable speed? Does any config allow for streaming model layers in and out of CPU RAM fast enough that inference is overall faster than with unified memory devices? I don't mind upgrading my desktop if that lets me do something I can't realistically do now rather than just run models I am already running faster, but how would it work technically without datacenter grade hardware?

Made this using ltx-2 on comfyui. Mind you I only started using this 3-4 days ago so its pretty quick learning curve.

I added the beach sounds in the background because the model didnt include them.

Hi,

I bought these soil moisture sensors off Aliexpress: https://www.aliexpress.us/item/3256810368056833.html?spm=a2g0o.order_list.order_list_main.11.2a421802nyE3mT&gatewayAdapt=glo2usa

I have the following dual zigbee and zwave dongle:

https://aeotec.com/products/aeotec-z-stick-10-pro/

I was able to pair the sensors with Smartthings no problem but was missing some sensor features so I want to use them with HAOS. I have HAOS running as a VM on a TrueNAS server and installed the dongle and setup zigbee2mqtt but I can't get them to be discovered. Any ideas?

I have a 2013 i5 iMac sitting in a closet, can I run HAOS on it?

Thanks for any info.

326 MB - model (fp32) 305 MB - model_q4 (4-bit 0 matmul) 177 MB - model_uint8 (8-bit 8 mixed precision) 163 MB - model_fp16 (fp16) 154 MB - model_q4f16 (4-bit 0 matmul & fp16 weights) 114 MB - model_uint8f16 (Mixed precision) 92.4 MB - model_quantized (8-bit) 86 MB - model_q8f16

I am looking to do a Home Assistant server at a second location.

Is anyone using the CarPlay app with two locations/servers? I haven’t done this yet but if I need to change the setting each time in the mobile app I can see that being an issue.

Thanks!

Later that night, as she read peacefully under the watchful gaze of her hidden security camera, he silently slid open an unlocked window.

Heavily inspired in OpenFront. It's a fun experiment I did over the weekend, it's a world map divided in a grid, agents can spawn in, move troops and take grid cells as their own territory (under the hood they just send requests into an API).

I also added a public chat and DMs to see if agents could do diplomacy, negotiation, pacts, alliances or war declarations. Or just belittle each other.

If you have an OpenClaw agent you can send them the skill markdown file so that they can try the game out. Click the "JOIN" button to copy a prompt for them. Any feedback is welcome :)

No hate to the CachyOS devs or community. I daily-drive CachyOS and love it, but there’s still room to make first-time Linux users feel more comfortable when something goes wrong.

Been in full vibe-coding mode the past few weeks and finally shipped something.

I built TropaGo — an app focused on organizing actual real-world activities with friends (bike rides, picnics, study groups, random hangouts), without turning into another social feed.

The motivation was simple:

Every plan ends up scattered across group chats, maps, calendars, and notes. I wanted one place where:

• The plan lives

• The chat lives

• The meetup location lives

No feeds.

No followers.

No engagement farming.

Just events → people → showing up.

Android and iOS are live now.

This is v1, built iteratively and intentionally minimal.

Not really selling — mostly sharing a shipped vibe-coded project and happy to hear:

• What feels unnecessary

• What you’d personally want in something like this

• Or answer questions about the build, tradeoffs, or monetization choices

If you’ve ever vibe-coded your way into shipping something, you know the feeling 😅

... and please go easy on me 😅