I am on the verge of just giving up. I've followed RyanOnTheInside and Skill Destiny's YT tutorials to a T, even using their same training parameters...for nothing. No matter the learning settings or the epochs, today I just got angry and overtrained a 14-song orchestral dataset with 1600 epochs and 20k steps, and I had to put the LORA strength to 2.0 to BARELY hear the style I trained.

So, what is going on? What am I doing wrong? I put 14 songs in WAV format in a folder and let the training do the rest, just like Ryan and the other guy do. But my Loras sound like ass. Do I need to split songs into 30-second chunks, do I need to do a backflip and recite the bible in reverse mid-air and land perfectly on the floor to be blessed with a working Lora?

I was so desperate that I downloaded and trained Loras using Side-step...and I got the same result, nothing. Like running a normal Lora at 0.1 strength. I also tried the SFT ComfyUI implementation, but sorry to the creator, but it sounds like a toaster having a stroke, even using his custom sampler.

This is an example of the JSON auto-generated by my workflow:

{

"id": "sample_0001",

"filename": "sample_0001.pt",

"audio_path": "E:\\ace-training\\music\\epicmusic\\02. Destiny.wav",

"caption": "A hypnotic and continuous loop of a synthesized arpeggio forms the entirety of this instrumental piece. The sound has a distinct lo-fi, chiptune character, reminiscent of classic video game soundtracks, with a slightly bit-crushed texture. The melodic sequence repeats without variation, creating a mesmerizing and slightly melancholic atmosphere before cutting off abruptly.",

"duration": 165.432,

"bpm": 125,

"keyscale": "E major",

"is_instrumental": true

},

Am I the only one? Am I going insane? My computer is an ultra i9, 64 GB RAM, RTX 5080 16 GB.

I made an open-source agent that 3D's stuff for you.

It's not perfect, but good enough for small functional stuff around the house.

Hey everyone, I’m building a document generation flow in Lovable, and I’m trying to achieve something very specific. I have a predefined document template (Word-style) where: • The header and footer must remain exactly the same • Only the main body content should be replaced dynamically using AI • The final export (PDF/Word) should be pixel-perfect, matching the original template layout Right now, when I try basic templating, the formatting sometimes affects the header/footer or breaks the structure during export. What I’m trying to achieve: • Lock header & footer (no changes at all) • Replace only specific content sections (like placeholders) • Maintain exact layout consistency in exports Questions: 1. Is there a way in Lovable to lock header/footer sections? 2. What’s the best way to use placeholders or bindings so AI only updates the body? 3. How do you ensure consistent Word/PDF output without layout shifts? 4. Any best practices for template-driven document generation like this? If anyone has implemented something similar or has suggestions, I’d really appreciate it 🙏 Thanks!

My family runs a clothing store in Jaipur. Like most small retail shops in India, their entire customer interaction happens on WhatsApp.

Every day, my brother was handling the same messages manually:

• "Kya available hai?" (What's available?)
• "Budget 5000 hai, kya dikhao ge?" (Budget 5000, what can you show me?)
• The same category and budget questions from 20 different people.
• Customers waiting 30 minutes for a product link, giving up, and going elsewhere.

He was running Instagram to bring leads in. The leads were coming. But there was nothing on the other end to handle them. Just a phone and one person replying to everything.

I'd been learning n8n and building small AI workflows for a while. I thought: this is exactly the problem automation is supposed to solve.

What I didn't expect was how long it would take.

Version 1 was embarrassing. A basic webhook that sent a canned reply. Fine for testing, useless for real customers.

The real problem hit around version 3. A customer sends "hi", the agent greets them, they say they want something, the agent jumps straight to asking for their name and budget. Same customer messages the next day. The agent has no idea who they are.

No memory. No routing. No sense of where a customer is in their journey.

I started over properly.

The final system: 44 nodes, 2 AI agents

Entry layer (before AI even runs):

Every incoming WhatsApp message passes through a filter first:

• Is this from the store's own number? Ignore.
• Is it from a group chat? Ignore.
• Did the customer send "START" or "STOP"? Route separately.
• Is this number on an exclusion list (Friend/STOP role in Google Sheets)? Block.

Only after all of that does the message go anywhere useful. This alone cut a lot of noise.

The status router (the part that took the most time):

Before any agent runs, the system fetches the customer's current status from Google Sheets. That status is one of:

• New Lead
• Follow-up
• Order Booking
• Product Not Found
• Complaint

Status is "Order Booking"? The message goes directly to the Order Booking Agent, skipping the main agent completely. Customer sends exactly "PP" (short for "price please")? Also routes to the Order Agent, but in a price-lookup mode.

Everything else goes to the Main Sales Agent.

Getting this routing right took weeks. The edge cases were brutal. A customer mid-order should not be re-greeted by the main agent. A customer who just confirmed "Haan" (yes) and is waiting for order details should not get the intent detection flow again. It sounds obvious when I say it. It is not obvious when you're building it.

The Main Sales Agent (8 stages):

One AI agent, one long system message, 8 stages of a real sales conversation:

1. Greeting (once only, never repeated mid-conversation)
2. Intent Detection (no lead capture until buying intent is clear)
3. Product Availability (searches Pinecone vector store before answering)
4. Lead Capture (Name, City, Budget, Category, Occasion)
5. Product Link Sharing (max 3 links per message, fetched from Google Sheets by Category + Budget)
6. Order Intent Handoff (the agent sets status to "Order Booking" and stops, never confirms itself)
7. Price Query (real price pulled from Item Price sheet by Item Code, never assumed)
8. FAQ + Human Handoff (Pinecone search for policy questions, STOP keyword exits the flow)

Two things the main agent can never do: confirm an order and make up a price. If it doesn't have the price, it says so. Order confirmation only happens in the next agent.

The Order Booking Agent:

A separate dedicated agent. Takes over once the customer is ready to buy.

Collects: Item Code, delivery date, any special preferences. Displays an order summary. Waits for the customer to type "FINAL". Only then does it write the order to the Orders sheet.

It also handles a "PP Mode" where customers jump straight to price inquiry by sending "PP", get the exact price from the sheet, and can then confirm or exit.

The business notification system:

When the main agent says something like "team aapse jald contact karegi" (team will contact you soon), a third agent picks up the output, pulls the full customer record and any order details from Google Sheets, and sends a structured summary directly to the store's WhatsApp number. The owner gets the full picture immediately without hunting for context.

Tech Stack:

• n8n (self-hosted) for orchestration
• OpenAI GPT-4o for both agents
• Pinecone for FAQ vector search
• Google Sheets as the database (Leads, Orders, Product Catalog, Item Prices)
• WhatsApp Cloud API for messaging
• Shared buffer memory window across all three agents

It's been running with real customers for a few weeks. Not flawless. The AI still occasionally asks for something it already has. But the main flow works, and my brother is no longer stuck on WhatsApp for hours every day.

The thing that surprised me most: the AI was not the hard part. Designing the state machine was. Knowing which agent should handle a message, what that customer already told us, and what happens when they switch context mid-conversation is a much harder problem than writing a good system prompt.

If I were starting over, I'd draw the routing logic on paper before touching n8n at all.

Attaching screenshots of the workflow canvas below. Happy to answer questions on specific nodes or decisions.

What would you have done differently?

So LTX-2 itself obviously has a hard time with loras, maybe most are not trained right? It seems the model will do whatever you want but when it comes to loras and or certain specific motions or asthetics it changes the output entirely. Its obvious front the live preview nodes. Is it Gemma filters secretly saying no under the hood and the base model changing the Gen or is it LTX itself or underlying text encoder?

Where do we go from here?

It seems the only way to get exactly what you want out of these DiTs is to train the actual model itself but that comes at massive cost.

Compared to Wan 2.2s freedom LTX is severely underwhelming and is made to intentionally be hard to train for.

AI stocks have taken a hit recently and a lot of people are wondering if it is time to get out. History might have the answer.

Past tech cycles show that only the strongest firms survive a sectorwide shakeout. The dot com boom gave us Amazon and Google but left behind countless companies that disappeared.

The old rule is to only hold the number one or two players in any niche. The rest are risky when things get tough.

Another thing to consider is that AI is now eating into traditional software models by offering similar tools at a fraction of the cost. If AI can replace a company's core product, a recovery becomes much harder.

So maybe hold the leaders but be careful with the rest. Anyone else rethinking their AI holdings right now?

I wrote this Python script to download (or attempt to) every model file that is called by the built-in templates as of the latest released version of ComfyUI today (25th March 2026). It only downloads models used by non-API related templates.

I haven't verified every single one and of course model files move around/get deleted by HF so this will need maintaining by me going forward. The model files are downloaded into their appropriate subfolders. No moving around required.

You don't have to download ALL. Has a menu system where you can choose categories.

Helpful?

https://github.com/NJToolsDev/ComfyUI-Template-Model-Downloader

Ladies and Gents, if you don't like the way ChatGPT "talks" to you, I'd recommend you update your memories so it responds more in a style you want to hear. These are mine, just as an example. I don't get any more click bait finishers, no "you're such a special snowflake", no "....and that's rare...." or any of that fluffy stuff. If you like all that, disregard all of this but I see alot of complaining on here about clickbait stuff (which IS annoying) but it IS fixable. Just take 5-10 minutes and customize things to how you want. And if you don't know how to do this, in any chat (or create a new one just for memories), simply type (or say) "Update saved memories to reflect I don't want ANY click-bait style closing statements or "if you want" questions....." (or something like that). You might have to do this a time or two, but it'll eventually stop. Mine has, at least. I'd also recommend trying out the different "base style and tone" options in the Personalization menu. I, personally, like Cynical for a lot of things because the way it talks is more my style (To the point, with a dash of sarcasm if appropriate) but mess around with them to find something you like. If you already know this, then great, but I've seen enough complaints on here that I start to wonder if there's people who don't know you CAN do something about the things you don't like.

I'm just doing some fine post-process work in the latest version of CUI.

But I've noticed that the rendering looks a bit dull side by side with Photoshop, it's not as contrasty. Subtle but noticeable.

And the images get blurred, so what looks ok zoomed in inside CUI is a bit aliased in Photoshop, like over-sharp. It's hard to describe but once the pixels get over a certain size it's like CUIs interface is filtering them quite heavily.

I'm not sure what PS is doing in the GUI as you zoom in, I assume it's stepping the scaling intervals so pixels remain a whole amount of pixels across.

This is the latest CUI, in old system and nodes 2.0.

I'll test again in my older version of CUI I have also.

But was curious if anyone else had noticed this in CUI? If there are some setting somewhere to make it better and the image rendering more representative of reality.

I was just about to work through my latest project in CUI (3,000 images to process), but straight away this is not reassuring because the viewport rendering just isn't representing reality... both in pixel appearance and possibly in colour rendering too?

Thanks

Their paper: https://arcprize.org/media/ARC_AGI_3_Technical_Report.pdf

On page 11:

This scoring function is called RHAE (Relative Human Action Efficiency), pronounced “Ray”. The procedure can be summarized as follows:

• “Score the AI test taker by its per-level action efficiency” - For each level that the test taker completes, count the number of actions that it took.

• “As compared to human baseline” - For each level that is counted, compare the AI agent’s action count to a human baseline, which we define as the second-best human action action. Ex: If the secondbest human completed a level in only 10 actions, but the AI agent took 100 to complete it, then the AI agent scores (10/100)² for that level, which gets reported as 1%. Note that level scoring is calculated using the square of efficiency.

• “Normalized per environment” - Each level is scored in isolation. Each individual level will get a score between 0% (very inefficient) 100% (matches or surpasses human level efficiency). The environment score will be a weighted-average of level score across all levels of that environment.

• “Across all environments” - The total score will be the sum of individual environment scores divided by the total number of environments. This will be a score between 0% and 100%.

So it's measuring "efficiency squared". So if a human solves the level in 10 moves but the AI takes 11, then the score is reported as (10/11)² = 83%. If the AI solves it in 9 moves (beating the human), then the score is reported at 100% (not above 100%). I think this is somewhat misleading because the average person reading headlines would've expected the same as prior ARC benchmarks but it's apples to oranges

Also note from page 13 that they have a hard cutoff at 5x human performance per level (so their example of 10 and 100 doesn't even work because they would've cut it off at 50 and just reported 0).

Note that since each level has a score from 0% to 100% (aka if an AI is more efficient than the human, they will only get a score of 100% and not exceeding it), getting a score of 100% will only be possible if the AI is more efficient than the human at ALL tasks. If the AI is like twice as efficient as a human in 99% of tasks but only 99% as efficient as a human in 1% of tasks, it would be reported as a < 100% score. Oh and levels have different weights in the scores.

Also in page 14:

the official leaderboard will not use a harness to report official scores

So it's just text in text out.

I question this because all of the fuss about AI agents in the last 3-4 months or so is because of the harness of codex and Claude Code. For instance Claude can now take control of your computer - but that won't be tested for (even if it means higher efficiency on ARC AGI 3).

From page 15:

ARC-AGI 3 system prompt “You are playing a game. Your goal is to win. Reply with the exact action you want to take. The final action in your reply will be executed next turn. Your entire reply will be carried to the next turn.”

The scores are also different compared to the web leaderboard

Gemini 3.1 Pro Preview 0.37% (web shows 0.2%)

GPT 5.4 (High) 0.26% (web shows 0.3%)

Opus 4.6 (Max) 0.25% (web shows 0.2%)

From page 17-18

The human efficiency of beating ARC-AGI-3 is measured by the number of actions it took to complete the environment. Because all human evaluations were conducted as first-run attempts, this data allows us to measure how efficiently humans solve each environment when encountering it for the first time. We track three reference points

• Optimal playthrough: Empirical estimate of the lower bound on the number of actions needed to solve the environment (once the environment’s mechanics and goals are already fully understood.)

• Best first-run playthrough: Best first-run human playthrough aggregated per level. It combines the fewest actions achieved by any test participant on each individual level on a first run, regardless of whether they came from the same person.

• Human baseline: Second-best first-run human playthrough. This is what we use as the human baseline in the official score computation.

I saw a number of people asking what exactly is the human baseline - so 100% is measured at the second best human player (there were 486 players btw). In that case, if YOU as a human did the entire benchmark, I wonder what YOUR score would've been? Almost assuredly WAY lower than 100% by their efficiency calculation, because it matters not if you found the puzzle easy - if you were worse than the 2nd best human run on this then your score will be HEAVILY penalized. Say the 2nd best score for a level was 10. You did it in 12 and say you found the puzzle "easy". Well your score for that level would've been (10/12)² = 69% even though you found it "easy". Oh and it must be your first try at the level.

Is this why Microsoft keeps falling in stock price? Are there agents building a new and better operating system? They built a c compiler in a few days. Why not an operating system in a few weeks?

Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions. One popular method involves submitting the same prompt multiple times to see if the model generates the same answer.

But this method measures self-confidence, and even the most impressive LLM might be confidently wrong. Overconfidence can mislead users about the accuracy of a prediction, which might result in devastating consequences in high-stakes settings like health care or finance.

I just had a couple of updates appear for some battery powered devices, I have 2 of them. One started, didn't appear to complete and then the notification has vanished.

Initiated the second, it got to 12.84%, hung and then it said the firmware entity had vanished.

I then popped off to have a read. All seems like a frankly excruciating process.

I found this not but couldn't get the "Issue Zigbee Command" button to enable in order to click it.

 New GUI method: [](https://github.com/zigpy/zigpy/wiki/OTA-Device-Firmware-Updates#new-gui-method) From device card right from "reconfigure" the three dots, `manage zigbee device`, select `OTA cluster` (id: 0x0019), click `commands`, select `image_notify` (id: 0x0000), and mark `payload_type`: `QueryJitter` and move the `query_jitter` slider so its not being at default zero. Note: Ignore "mandatory" `manufacturer_code`/`image_type`/`new_file_version` fields and still click grayed out "Issue Zigbee command" button. Click `Issue Zigbee command` and waking up the device by clicking one button if its one sleeping device so it can receiving the command. 

I've also added the following to my configuration.yaml, is this still a requirement?

zha: zigpy_config: ota: extra_providers: - type: z2m 

I was wondering if there was an aqara entry but couldn't find any info about it.

Lawyers for the Department of War and Anthropic sparred in a California federal court on Tuesday over Anthropic’s challenge to the Pentagon labeling it a “supply-chain risk” to national security and banning all government contractors from using the company’s sweeping AI tools. Anthropic is seeking an injunction barring enforcement of that order.

The case—which involves a historic first in that the Department of Defense, informally renamed the Department of War (DOW) by the Trump administration, labeled a U.S.-led business as a supply-chain risk to national security—is rooted in a contract negotiation that escalated quickly. The DOW wanted to add a blanket “all lawful use” clause to its contracts with the AI firm so the military could use Anthropic’s Claude tool for any legal purpose.

The presiding judge in the case expressed doubts about the sweeping authority the Pentagon had wielded in the case. Federal District Judge Rita Lin said she would issue a ruling on Anthropic’s legal challenge “in the next few days,” and spent Tuesday’s hearing asking the parties questions about their disagreement.

Read more: https://fortune.com/2026/03/24/anthropic-hegseth-trump-risk-ai-court-ruling/

Hi there,

Not sure if it’s a bug or feature, but when I’m configuring the HomeKit bridge in include mode, choose the desired domains (otherwise I cannot select the entities in the next step) and then select the entities I want to show in HomeKit, many entities (i.e. especially app entities I believe? E.g. “Zigbee2MQTT Restart”-Button) are brought to HomeKit despite not being selected.

How can I avoid bringing app entities to HomeKit which have not been selected?

Thanks!

Hi all,

I have existing motorized blinds that work through an RF remote only. Any ways to make these smart through home assistant?

Thanks

I'm pretty new to n8n I've created some basic workflows. I now want to generate a workflow for my ecommerce business. I used claude to build it but keep facing errors and have fixed a bunch but i'm stuck on node 8 and I have no credits left on claude to fix it and chat hasn't been that helpful...

Workflow goal: Use clothing flat and hanger images of clothes that I have taken and I want it to use the ai model I provide for it to generate front, back, side pose.

Problem: I am stuck on node 8.

There's an issue with the binary data/json I believe. I don't fully understand the errors.
Initially claude added nano banana as a webhook but I changed it to gemini.

I've attached the screenshot and the JSON is uploaded. I haven't changed anything past node 8 yet. I've included the screenshots of the images of the clothes and the ai model for context (this model already is wearing the clothing item but generally i'd have different clothing items uploaded to use that same model as a reference photo)

Also if you have a totally different suggestion on building something like this please let me know. I've watched plenty of tutorials but most don't include a loop and the photos aren't consistent i.e. the logo dissapears etc.

JSON FILE:
https://drive.google.com/file/d/1nxm-oOrgdzujRsnjMqdIPF_sbdWSwe4O/view?usp=sharing

https://preview.redd.it/o6a5isw4a8rg1.png?width=2080&format=png&auto=webp&s=48108cab3ea94524b6f3eec62891a39e07611a89

https://preview.redd.it/y176dtw4a8rg1.png?width=1822&format=png&auto=webp&s=1d16ceeb3169f7873de2abe95483e4c391b0162c

https://preview.redd.it/6wf9etw4a8rg1.png?width=2279&format=png&auto=webp&s=8d57a5a4bb0b940c386384accf75141a92c1ab3c

https://preview.redd.it/49ydoito98rg1.png?width=1324&format=png&auto=webp&s=ccd5ee27909915c813dfc3aec98094e2859c75e5

https://preview.redd.it/b3ul24aj98rg1.png?width=727&format=png&auto=webp&s=e0aa18662f9465538a34dfc6ad7b930773d588a2

{

"name": "My workflow 2",

"nodes": [

{

"parameters": {},

"id": "77ebd72a-ea40-4b39-b7c4-74be0eb4632f",

"name": "1. Manual Trigger (Click to Start)",

"type": "n8n-nodes-base.manualTrigger",

"position": [

-368,

128

],

"typeVersion": 1,

"notes": "Click 'Test Workflow' to start. Processes everything currently in your INPUT batch folder."

},

{

"parameters": {

"resource": "fileFolder",

"filter": {

"folderId": {

"__rl": true,

"value": "1ilE7s1Fe8Sq-M5Hb4LC0of3kb58fGbWT",

"mode": "list",

"cachedResultName": "Organized prima",

"cachedResultUrl": "https://drive.google.com/drive/folders/1ilE7s1Fe8Sq-M5Hb4LC0of3kb58fGbWT"

}

},

"options": {}

},

"id": "7dc8c110-4881-4d9e-8ba5-76936b8bb245",

"name": "2. List All Files in INPUT Folder",

"type": "n8n-nodes-base.googleDrive",

"position": [

-48,

128

],

"typeVersion": 3,

"credentials": {

"googleDriveOAuth2Api": {

"id": "cAIq7xOPWCA017Jw",

"name": "Google Drive account"

}

},

"notes": "Fetches all files from your INPUT folder. Set INPUT_FOLDER_ID in n8n Variables to your batch folder ID."

},

{

"parameters": {

"options": {}

},

"id": "e098b72b-6d14-4818-aac8-7c00b2cf6c91",

"name": "3. Process One File at a Time",

"type": "n8n-nodes-base.splitInBatches",

"position": [

256,

128

],

"typeVersion": 3,

"notes": "Loops through each file one by one so they don't interfere with each other."

},

{

"parameters": {

"jsCode": "if (!$json || !$json.name) {\n return [];\n}\n\nconst fileName = $json.name;\nconst fileId = $json.id;\n\nconst fullName = fileName.toLowerCase();\n\nlet imageType = 'unknown';\nif (fullName.includes('front-flat')) imageType = 'front-flat';\nelse if (fullName.includes('back-flat')) imageType = 'back-flat';\nelse if (fullName.includes('hanger-front')) imageType = 'hanger-front';\nelse if (fullName.includes('hanger-back')) imageType = 'hanger-back';\nelse if (fullName.includes('closeup') || fullName.includes('close-up')) imageType = 'closeup';\nelse if (fullName.includes('model-reference')) imageType = 'model-reference';\n\nlet productName = fileName.replace(/\\.(jpg|jpeg|png|webp)$/i, '');\nproductName = productName\n .replace(/-front-flat$/i, '')\n .replace(/-back-flat$/i, '')\n .replace(/-hanger-front$/i, '')\n .replace(/-hanger-back$/i, '')\n .replace(/-closeup$/i, '')\n .replace(/-close-up$/i, '')\n .replace(/-model-reference$/i, '')\n .replace(/^model-reference$/i, 'batch');\n\nreturn [\n {\n json: {\n fileName: fileName,\n fileId: fileId,\n productName: productName,\n imageType: imageType\n }\n }\n];"

},

"id": "2692db56-98e2-42cd-84f8-921b6e2ca262",

"name": "4. Parse Filename & Detect Image Type",

"type": "n8n-nodes-base.code",

"position": [

496,

96

],

"typeVersion": 2

},

{

"parameters": {

"conditions": {

"string": [

{

"value1": "={{ $json.imageType }}",

"operation": "notEqual",

"value2": "unknown"

}

]

},

"options": {}

},

"id": "bb629ffa-0994-4ff0-a2a8-7e4a73f84860",

"name": "5. Is Valid Image Type?",

"type": "n8n-nodes-base.if",

"position": [

768,

128

],

"typeVersion": 2.3,

"notes": "Files named incorrectly go to the NEEDS-RENAME folder. Correctly named files continue."

},

{

"parameters": {

"operation": "move",

"fileId": "={{ $json.fileId }}",

"driveId": {

"__rl": true,

"mode": "list",

"value": "My Drive"

},

"folderId": {

"__rl": true,

"value": "={{ $vars.NEEDS_RENAME_FOLDER_ID }}",

"mode": "id"

}

},

"id": "e4682dda-a1da-489c-a2ae-391bb37a7b96",

"name": "5a. Move to NEEDS-RENAME Folder",

"type": "n8n-nodes-base.googleDrive",

"position": [

1040,

304

],

"typeVersion": 3,

"credentials": {

"googleDriveOAuth2Api": {

"id": "cAIq7xOPWCA017Jw",

"name": "Google Drive account"

}

},

"notes": "Incorrectly named files get moved here so you can fix and re-run."

},

{

"parameters": {

"operation": "download",

"fileId": "={{ $json.fileId }}",

"options": {}

},

"id": "4bf67447-a0ea-4440-a739-783d5bda0c97",

"name": "5b. Download Image from Drive",

"type": "n8n-nodes-base.googleDrive",

"position": [

1024,

64

],

"typeVersion": 3,

"credentials": {

"googleDriveOAuth2Api": {

"id": "cAIq7xOPWCA017Jw",

"name": "Google Drive account"

}

}

},

{

"parameters": {

"jsCode": "const items = $input.all();\n\nfor (const item of items) {\n const binary = item.binary?.data;\n\n if (!binary || !binary.data) {\n throw new Error('No binary data found');\n }\n\n const base64 = Buffer.from(binary.data, 'base64').toString('base64');\n\n item.json.base64Image = `data:${binary.mimeType};base64,${base64}`;\n}\n\nreturn items;"

},

"id": "0173fa40-3c0d-4ec8-b5e1-7da80add5b1c",

"name": "6. Convert to Base64",

"type": "n8n-nodes-base.code",

"position": [

1264,

128

],

"typeVersion": 2

},

{

"parameters": {

"conditions": {

"options": {

"caseSensitive": true,

"leftValue": "",

"typeValidation": "strict",

"version": 3

},

"conditions": [

{

"id": "a0b2a230-8cd5-469b-afdb-cdb9168fc8d0",

"leftValue": "",

"rightValue": "",

"operator": {

"type": "string",

"operation": "equals",

"name": "filter.operator.equals"

}

}

],

"combinator": "and"

},

"options": {}

},

"id": "84064f1c-c289-41fa-84f9-57f15465c138",

"name": "7. Is This a Front Flat?",

"type": "n8n-nodes-base.if",

"position": [

1488,

128

],

"typeVersion": 2.3,

"notes": "Front-flat triggers the full generation pipeline. All other image types get stored as references."

},

{

"parameters": {

"mode": "set",

"options": {}

},

"id": "fb0666f8-ca2d-45e8-bc18-5f1d7732a56e",

"name": "7a. Store Reference Image (not front-flat)",

"type": "n8n-nodes-base.set",

"position": [

1696,

320

],

"typeVersion": 3.4,

"notes": "Stores back-flat, hangers, closeup, and model-reference in workflow memory keyed by productname_type."

},

{

"parameters": {

"jsCode": "const items = $input.all();\nconst results = [];\n\nfor (const item of items) {\n const productName = item.json.productName;\n\n if (!item.binary || !item.binary.data) {\n throw new Error('Missing binary image');\n }\n\n const prompt = `Professional ecommerce front-facing photo of a Middle Eastern male model wearing this exact garment. White background. Full body. Preserve ALL garment details exactly. Gym wear styling.`;\n\n results.push({\n json: {\n productName: productName,\n shotType: \"front\",\n prompt: prompt\n },\n // This line is the \"bridge\" that lets the image reach the API\n binary: item.binary \n });\n}\n\nreturn results;"

},

"id": "32886495-352f-47f6-b76e-1991d310568c",

"name": "7b. Build Front Shot Payload",

"type": "n8n-nodes-base.code",

"position": [

1696,

128

],

"typeVersion": 2

},

{

"parameters": {

"method": "POST",

"url": "https://generativelanguage.googleapis.com/v1/models/gemini-2.5-flash:generateContent",

"sendQuery": true,

"queryParameters": {

"parameters": [

{

"name": "key",

"value": "APIKEY"

}

]

},

"sendBody": true,

"specifyBody": "={\n \"contents\": [\n {\n \"parts\": [\n {\n \"text\": \"Professional ecommerce front-facing photo of a Middle Eastern male model wearing this exact garment. White background. Preserve ALL details exactly. Full body. Gym wear styling.\"\n },\n {\n \"inline_data\": {\n \"mime_type\": \"image/jpeg\",\n \"data\": \"={{ $binary.data.data }}\"\n }\n }\n ]\n }\n ]\n}",

"bodyParameters": {

"parameters": [

{}

]

},

"options": {}

},

"id": "86994868-2bcf-4295-bf3d-cdd3de34c56e",

"name": "8. Call NanaBanana API",

"type": "n8n-nodes-base.httpRequest",

"position": [

1936,

128

],

"typeVersion": 4.4,

"notes": "Sends the generation request. Returns a taskId for polling."

},

{

"parameters": {},

"id": "6393f6aa-ed25-4dac-b401-9260be4f6056",

"name": "9. Extract Task ID",

"type": "n8n-nodes-base.code",

"position": [

2144,

128

],

"typeVersion": 2

},

{

"parameters": {},

"id": "71069f48-e447-4448-9519-584d9768d377",

"name": "10. Wait 5 Seconds",

"type": "n8n-nodes-base.wait",

"position": [

2368,

128

],

"typeVersion": 1.1,

"webhookId": "f23c1fb2-b629-4515-bd7e-9594aa1db9c4"

},

{

"parameters": {

"url": "=https://www.nananobanana.com/api/v1/generate/{{ $json.taskId }}",

"sendHeaders": true,

"headerParameters": {

"parameters": [

{

"name": "Authorization",

"value": "=Bearer {{ $vars.NANABANANA_API_KEY }}"

}

]

},

"options": {}

},

"id": "15c33b98-4111-4640-bb7b-969c2719d184",

"name": "11. Poll Task Status",

"type": "n8n-nodes-base.httpRequest",

"position": [

2576,

128

],

"typeVersion": 4.4

},

{

"parameters": {

"conditions": {

"string": [

{

"value1": "={{ $json.status || $json.data?.processingStatus }}",

"operation": "equal",

"value2": "completed"

}

]

},

"options": {}

},

"id": "49276b2d-bdb4-4f42-ba6a-d2a031549201",

"name": "12. Is Generation Complete?",

"type": "n8n-nodes-base.if",

"position": [

2800,

128

],

"typeVersion": 2.3

},

{

"parameters": {},

"id": "f20be5dc-20ff-4e27-8066-f2d6316eb2a7",

"name": "12a. Not Done Yet — Retry",

"type": "n8n-nodes-base.code",

"position": [

3024,

320

],

"typeVersion": 2

},

{

"parameters": {

"url": "={{ $json.output_url || $json.imageUrl || $json.image_url || $json.data?.outputImageUrls?.[0] || $json.result }}",

"options": {

"response": {

"response": {

"responseFormat": "file"

}

}

}

},

"id": "f3f147fa-0b83-4a0e-96d0-3b72f7708e87",

"name": "12b. Download Generated Image",

"type": "n8n-nodes-base.httpRequest",

"position": [

3024,

128

],

"typeVersion": 4.4

},

{

"parameters": {

"name": "={{ $('9. Extract Task ID').first().json.productName + '-' + $('9. Extract Task ID').first().json.shotType + '-generated.jpg' }}",

"driveId": {

"__rl": true,

"mode": "list",

"value": "My Drive"

},

"folderId": {

"__rl": true,

"value": "={{ $vars.OUTPUT_FOLDER_ID }}",

"mode": "id"

},

"options": {}

},

"id": "a2c94368-5c61-4df1-b644-29423913cdbc",

"name": "13. Save to OUTPUT Folder",

"type": "n8n-nodes-base.googleDrive",

"position": [

3248,

128

],

"typeVersion": 3,

"credentials": {

"googleDriveOAuth2Api": {

"id": "cAIq7xOPWCA017Jw",

"name": "Google Drive account"

}

}

},

{

"parameters": {},

"id": "6e5103de-0094-4646-a372-fbcae9ad67b6",

"name": "14. Build Back Shot Payload",

"type": "n8n-nodes-base.code",

"position": [

3456,

128

],

"typeVersion": 2

},

{

"parameters": {

"method": "POST",

"url": "https://www.nananobanana.com/api/v1/generate",

"sendHeaders": true,

"headerParameters": {

"parameters": [

{

"name": "Authorization",

"value": "=Bearer {{ $vars.NANABANANA_API_KEY }}"

},

{

"name": "Content-Type",

"value": "application/json"

}

]

},

"sendBody": true,

"specifyBody": "json",

"jsonBody": "={{ JSON.stringify($json.apiPayload) }}",

"options": {}

},

"id": "d8b9dafc-5d31-4034-b802-94294ec74534",

"name": "15. Call NanaBanana API (Back Shot)",

"type": "n8n-nodes-base.httpRequest",

"position": [

3680,

128

],

"typeVersion": 4.4

},

{

"parameters": {},

"id": "ddbe79a6-9eeb-4e4e-aa16-7d2b4a28c056",

"name": "16. Extract Back Shot Task ID",

"type": "n8n-nodes-base.code",

"position": [

3904,

128

],

"typeVersion": 2

},

{

"parameters": {},

"id": "9e6e65d8-8de8-47b2-9a59-8f8529734879",

"name": "17. Wait 5s (Back)",

"type": "n8n-nodes-base.wait",

"position": [

4128,

128

],

"typeVersion": 1.1,

"webhookId": "08976cd8-e117-4578-a63b-d3117f3a1e7a"

},

{

"parameters": {

"url": "=https://www.nananobanana.com/api/v1/generate/{{ $json.taskId }}",

"sendHeaders": true,

"headerParameters": {

"parameters": [

{

"name": "Authorization",

"value": "=Bearer {{ $vars.NANABANANA_API_KEY }}"

}

]

},

"options": {}

},

"id": "625712d2-5b06-4109-b714-dde82185ec5f",

"name": "18. Poll Back Shot Status",

"type": "n8n-nodes-base.httpRequest",

"position": [

4336,

128

],

"typeVersion": 4.4

},

{

"parameters": {

"conditions": {

"string": [

{

"value1": "={{ $json.status || $json.data?.processingStatus }}",

"operation": "equal",

"value2": "completed"

}

]

},

"options": {}

},

"id": "946ebb03-d8ae-4e32-9983-201edc1dd0f3",

"name": "19. Back Shot Complete?",

"type": "n8n-nodes-base.if",

"position": [

4560,

128

],

"typeVersion": 2.3

},

{

"parameters": {},

"id": "293d7ee9-9cef-4111-8eed-58297fc00364",

"name": "19a. Retry Back Shot",

"type": "n8n-nodes-base.code",

"position": [

4784,

320

],

"typeVersion": 2

},

{

"parameters": {

"url": "={{ $json.output_url || $json.imageUrl || $json.data?.outputImageUrls?.[0] }}",

"options": {

"response": {

"response": {

"responseFormat": "file"

}

}

}

},

"id": "fb88ed72-fedd-4a7f-8d85-d7c8cd9fc13d",

"name": "19b. Download Back Shot",

"type": "n8n-nodes-base.httpRequest",

"position": [

4784,

128

],

"typeVersion": 4.4

},

{

"parameters": {

"name": "={{ $('16. Extract Back Shot Task ID').first().json.productName + '-back-generated.jpg' }}",

"driveId": {

"__rl": true,

"mode": "list",

"value": "My Drive"

},

"folderId": {

"__rl": true,

"value": "={{ $vars.OUTPUT_FOLDER_ID }}",

"mode": "id"

},

"options": {}

},

"id": "e1f32d0b-3799-42b7-8a39-118b099e83a3",

"name": "20. Save Back Shot to OUTPUT",

"type": "n8n-nodes-base.googleDrive",

"position": [

5008,

128

],

"typeVersion": 3,

"credentials": {

"googleDriveOAuth2Api": {

"id": "cAIq7xOPWCA017Jw",

"name": "Google Drive account"

}

}

},

{

"parameters": {},

"id": "59215609-0686-4fa3-be43-783f1d3e5f9b",

"name": "21. Build Closeup Shot Payload",

"type": "n8n-nodes-base.code",

"position": [

5216,

128

],

"typeVersion": 2

},

{

"parameters": {

"method": "POST",

"url": "https://www.nananobanana.com/api/v1/generate",

"sendHeaders": true,

"headerParameters": {

"parameters": [

{

"name": "Authorization",

"value": "=Bearer {{ $vars.NANABANANA_API_KEY }}"

},

{

"name": "Content-Type",

"value": "application/json"

}

]

},

"sendBody": true,

"specifyBody": "json",

"jsonBody": "={{ JSON.stringify($json.apiPayload) }}",

"options": {}

},

"id": "4e8987b2-4634-4662-b4ed-8dbe3c0209a9",

"name": "22. Call NanaBanana API (Closeup)",

"type": "n8n-nodes-base.httpRequest",

"position": [

5440,

128

],

"typeVersion": 4.4

},

{

"parameters": {},

"id": "bc2cbc97-37cc-4b15-858e-082be2ae0437",

"name": "23. Extract Closeup Task ID",

"type": "n8n-nodes-base.code",

"position": [

5664,

128

],

"typeVersion": 2

},

{

"parameters": {},

"id": "211cba5d-050d-4ab7-9b33-521801e4c881",

"name": "24. Wait 5s (Closeup)",

"type": "n8n-nodes-base.wait",

"position": [

5888,

128

],

"typeVersion": 1.1,

"webhookId": "8354d792-1467-442d-8c87-e7b4e110acc2"

},

{

"parameters": {

"url": "=https://www.nananobanana.com/api/v1/generate/{{ $json.taskId }}",

"sendHeaders": true,

"headerParameters": {

"parameters": [

{

"name": "Authorization",

"value": "=Bearer {{ $vars.NANABANANA_API_KEY }}"

}

]

},

"options": {}

},

"id": "82ae6a09-eac1-436d-81e5-5478289b17ff",

"name": "25. Poll Closeup Status",

"type": "n8n-nodes-base.httpRequest",

"position": [

6096,

128

],

"typeVersion": 4.4

},

{

"parameters": {

"conditions": {

"string": [

{

"value1": "={{ $json.status || $json.data?.processingStatus }}",

"operation": "equal",

"value2": "completed"

}

]

},

"options": {}

},

"id": "9af565d6-ac92-4216-ae7e-b3e5298d7542",

"name": "26. Closeup Complete?",

"type": "n8n-nodes-base.if",

"position": [

6320,

128

],

"typeVersion": 2.3

},

{

"parameters": {},

"id": "599a219e-b465-4914-8c1e-d6617780a937",

"name": "26a. Retry Closeup",

"type": "n8n-nodes-base.code",

"position": [

6544,

320

],

"typeVersion": 2

},

{

"parameters": {

"url": "={{ $json.output_url || $json.imageUrl || $json.data?.outputImageUrls?.[0] }}",

"options": {

"response": {

"response": {

"responseFormat": "file"

}

}

}

},

"id": "673d291c-53c6-462e-87cb-551c92fedb3e",

"name": "26b. Download Closeup",

"type": "n8n-nodes-base.httpRequest",

"position": [

6544,

128

],

"typeVersion": 4.4

},

{

"parameters": {

"name": "={{ $('23. Extract Closeup Task ID').first().json.productName + '-closeup-generated.jpg' }}",

"driveId": {

"__rl": true,

"mode": "list",

"value": "My Drive"

},

"folderId": {

"__rl": true,

"value": "={{ $vars.OUTPUT_FOLDER_ID }}",

"mode": "id"

},

"options": {}

},

"id": "8ff87fd2-c151-48b6-8559-d25aedf615c3",

"name": "27. Save Closeup to OUTPUT",

"type": "n8n-nodes-base.googleDrive",

"position": [

6768,

128

],

"typeVersion": 3,

"credentials": {

"googleDriveOAuth2Api": {

"id": "cAIq7xOPWCA017Jw",

"name": "Google Drive account"

}

}

},

{

"parameters": {},

"id": "adfff8f9-62bc-4bf1-8a5c-b9112c38659a",

"name": "28. Done! Log Completion",

"type": "n8n-nodes-base.code",

"position": [

6976,

128

],

"typeVersion": 2,

"notes": "Workflow complete for this product. The SplitInBatches node will automatically move to the next product."

},

{

"parameters": {

"resource": "image",

"options": {}

},

"type": "@n8n/n8n-nodes-langchain.openAi",

"typeVersion": 2.1,

"position": [

2272,

272

],

"id": "8300d8e3-2135-43ac-a32e-735a43f63228",

"name": "Generate an image",

"credentials": {

"openAiApi": {

"id": "sJ9UzjOXW48JzMto",

"name": "OpenAi account"

}

}

}

],

"pinData": {

"1. Manual Trigger (Click to Start)": [

{

"json": {},

"pairedItem": {

"item": 0

}

}

]

},

"connections": {

"1. Manual Trigger (Click to Start)": {

"main": [

[

{

"node": "2. List All Files in INPUT Folder",

"type": "main",

"index": 0

}

]

]

},

"2. List All Files in INPUT Folder": {

"main": [

[

{

"node": "3. Process One File at a Time",

"type": "main",

"index": 0

}

]

]

},

"3. Process One File at a Time": {

"main": [

[],

[

{

"node": "4. Parse Filename & Detect Image Type",

"type": "main",

"index": 0

}

]

]

},

"4. Parse Filename & Detect Image Type": {

"main": [

[

{

"node": "5. Is Valid Image Type?",

"type": "main",

"index": 0

}

]

]

},

"5. Is Valid Image Type?": {

"main": [

[

{

"node": "5b. Download Image from Drive",

"type": "main",

"index": 0

}

],

[

{

"node": "5a. Move to NEEDS-RENAME Folder",

"type": "main",

"index": 0

}

]

]

},

"5b. Download Image from Drive": {

"main": [

[

{

"node": "6. Convert to Base64",

"type": "main",

"index": 0

}

]

]

},

"6. Convert to Base64": {

"main": [

[

{

"node": "7. Is This a Front Flat?",

"type": "main",

"index": 0

}

]

]

},

"7. Is This a Front Flat?": {

"main": [

[

{

"node": "7b. Build Front Shot Payload",

"type": "main",

"index": 0

}

],

[

{

"node": "7a. Store Reference Image (not front-flat)",

"type": "main",

"index": 0

}

]

]

},

"7b. Build Front Shot Payload": {

"main": [

[

{

"node": "8. Call NanaBanana API",

"type": "main",

"index": 0

}

]

]

},

"8. Call NanaBanana API": {

"main": [

[

{

"node": "9. Extract Task ID",

"type": "main",

"index": 0

},

{

"node": "Generate an image",

"type": "main",

"index": 0

}

]

]

},

"9. Extract Task ID": {

"main": [

[

{

"node": "10. Wait 5 Seconds",

"type": "main",

"index": 0

}

]

]

},

"10. Wait 5 Seconds": {

"main": [

[

{

"node": "11. Poll Task Status",

"type": "main",

"index": 0

}

]

]

},

"11. Poll Task Status": {

"main": [

[

{

"node": "12. Is Generation Complete?",

"type": "main",

"index": 0

}

]

]

},

"12. Is Generation Complete?": {

"main": [

[

{

"node": "12b. Download Generated Image",

"type": "main",

"index": 0

}

],

[

{

"node": "12a. Not Done Yet — Retry",

"type": "main",

"index": 0

}

]

]

},

"12a. Not Done Yet — Retry": {

"main": [

[

{

"node": "10. Wait 5 Seconds",

"type": "main",

"index": 0

}

]

]

},

"12b. Download Generated Image": {

"main": [

[

{

"node": "13. Save to OUTPUT Folder",

"type": "main",

"index": 0

}

]

]

},

"13. Save to OUTPUT Folder": {

"main": [

[

{

"node": "14. Build Back Shot Payload",

"type": "main",

"index": 0

}

]

]

},

"14. Build Back Shot Payload": {

"main": [

[

{

"node": "15. Call NanaBanana API (Back Shot)",

"type": "main",

"index": 0

}

]

]

},

"15. Call NanaBanana API (Back Shot)": {

"main": [

[

{

"node": "16. Extract Back Shot Task ID",

"type": "main",

"index": 0

}

]

]

},

"16. Extract Back Shot Task ID": {

"main": [

[

{

"node": "17. Wait 5s (Back)",

"type": "main",

"index": 0

}

]

]

},

"17. Wait 5s (Back)": {

"main": [

[

{

"node": "18. Poll Back Shot Status",

"type": "main",

"index": 0

}

]

]

},

"18. Poll Back Shot Status": {

"main": [

[

{

"node": "19. Back Shot Complete?",

"type": "main",

"index": 0

}

]

]

},

"19. Back Shot Complete?": {

"main": [

[

{

"node": "19b. Download Back Shot",

"type": "main",

"index": 0

}

],

[

{

"node": "19a. Retry Back Shot",

"type": "main",

"index": 0

}

]

]

},

"19a. Retry Back Shot": {

"main": [

[

{

"node": "17. Wait 5s (Back)",

"type": "main",

"index": 0

}

]

]

},

"19b. Download Back Shot": {

"main": [

[

{

"node": "20. Save Back Shot to OUTPUT",

"type": "main",

"index": 0

}

]

]

},

"20. Save Back Shot to OUTPUT": {

"main": [

[

{

"node": "21. Build Closeup Shot Payload",

"type": "main",

"index": 0

}

]

]

},

"21. Build Closeup Shot Payload": {

"main": [

[

{

"node": "22. Call NanaBanana API (Closeup)",

"type": "main",

"index": 0

}

]

]

},

"22. Call NanaBanana API (Closeup)": {

"main": [

[

{

"node": "23. Extract Closeup Task ID",

"type": "main",

"index": 0

}

]

]

},

"23. Extract Closeup Task ID": {

"main": [

[

{

"node": "24. Wait 5s (Closeup)",

"type": "main",

"index": 0

}

]

]

},

"24. Wait 5s (Closeup)": {

"main": [

[

{

"node": "25. Poll Closeup Status",

"type": "main",

"index": 0

}

]

]

},

"25. Poll Closeup Status": {

"main": [

[

{

"node": "26. Closeup Complete?",

"type": "main",

"index": 0

}

]

]

},

"26. Closeup Complete?": {

"main": [

[

{

"node": "26b. Download Closeup",

"type": "main",

"index": 0

}

],

[

{

"node": "26a. Retry Closeup",

"type": "main",

"index": 0

}

]

]

},

"26a. Retry Closeup": {

"main": [

[

{

"node": "24. Wait 5s (Closeup)",

"type": "main",

"index": 0

}

]

]

},

"26b. Download Closeup": {

"main": [

[

{

"node": "27. Save Closeup to OUTPUT",

"type": "main",

"index": 0

}

]

]

},

"27. Save Closeup to OUTPUT": {

"main": [

[

{

"node": "28. Done! Log Completion",

"type": "main",

"index": 0

}

]

]

},

"28. Done! Log Completion": {

"main": [

[

{

"node": "3. Process One File at a Time",

"type": "main",

"index": 0

}

]

]

}

},

"active": false,

"settings": {

"executionOrder": "v1",

"binaryMode": "separate",

"availableInMCP": false

},

"versionId": "1eadc896-f28d-49eb-ba0f-500d83dd62a2",

"meta": {

"templateCredsSetupCompleted": true,

"instanceId": "73b953ef64e9d36dbeb73a1b68bce071cf6c7d1b2d4b8cbb58bd26f729544fcb"

},

"id": "tpFABnR80NHs23ef",

"tags": []

}

I feel like I am going crazy. All of the pumps I buy allow liquid to just flow through them when off. All are labeled diaphragm pumps, and when they are off, they still allow liquid to flow through. This means that that when the mop is not being used, all the liquid in the tank just runs through the nozzles.

When I disassembled a Swiffer Powemop, I found a pump inside labeled DSB412-G141 (12V), and this pump somehow shuts its valves completely when it's off. You cant even blow air through it.

https://www.amazon.com/dp/B09ZX4TFNG?ref=fed_asin_title

https://www.amazon.com/gp/product/B07Y3DSZWB/ref=ox_sc_act_image_1?smid=A1BV5WVO8426GB&psc=1

They are both diaphragm pumps. What information about the pumps suggest that they will behave in this different way? Just looking for a pump that shuts its valve when off.

Thank you!!

https://preview.redd.it/otg2e7nlv7rg1.png?width=1263&format=png&auto=webp&s=877411ea49c8c69d5304aea45ea30130e5bcf2c6

This month, my team and I were building an app similar to Yuka, but with some extra integrations. The idea came from a simple problem: a lot of people throw food away because they do not know what to cook with the ingredients they already have.

So we thought: why not make an app that suggests recipes based on what you already have at home? It could also show the missing ingredients in nearby supermarkets and stuff like that.

At first, we wanted to go quite hard on AI. The idea was to generate recipes from ingredients and store them in our database too, so users could get a mix of fresh AI-generated recipes and previous ones already saved in the system.

But the more we thought about it, the more problems started appearing.

• First, how do you stop the AI from generating the same recipe over and over? The database could end up full of duplicates.
• Second, there is gonna be a point where generating more recipes with AI stops making sense, because you already have enough stored.
• Third, if you generate the recipe, you probably also want an image. And even if each call is not crazy expensive, once you scale it, it starts becoming a real cost problem.

I first thought about using another AI as a filter. Basically, checking a new recipe against the ones already stored and deciding whether it was different enough to save. It sounded interesting, but we were not really convinced.

Then we considered vector embeddings and semantic comparison. That also made sense, but honestly it was starting to get too complicated for what we actually needed.

We explained all this to our teacher, and he gave us a much simpler idea: forget generating everything with AI, just build the recipe database and filter recipes by ingredients.

That solved some things immediately. No constant AI cost. No weird duplicate generation problem. But then another issue appeared: filling a database with hundreds of recipes by hand is painfully slow.

And that was the moment it clicked for me.

Instead of using AI to generate endless recipes, I could use automation to build the database fast.

So I started thinking about a workflow that scrapes recipe websites, extracts only the exact data we need, and stores it directly. Give it a recipe URL, and the workflow does the rest. I still use AI a little for structuring the data, but that is way cheaper than full recipe + image generation.

So yeah, in the end, the solution I was looking for was not “more AI.” It was using automation in a smarter place.

One improvement I still want to add: storing all scraped URLs in a Google Sheet first, so I can check them before scraping and avoid duplicates there too.

This project honestly made me realize that sometimes the best AI decision is knowing where not to use AI.

Have any of you had a project where the best solution was actually less AI, not more?

OpenAI has taken down their Sora 2 video model, presumably because it wasn't yielding a meaningful return and was simply burning money.

They also told the BBC that they have discontinued Sora 2 so that they can focus on other developments, such as robotics "that will help people solve real-world, physical tasks".

From what I can gather, they won't be focusing on developing video models. If that's the case, why not release the weights to disrupt the video AI market rather than letting the model fade into obscurity? Sora 2 might not be the best video model (and even if it is, it wouldn't be for long), but it would be the best open-weight video model by far.

Many people in the 90s and early 2000s grew up with the family computer that was basically the family’s main point of storing all sorts of files and interacting with the digital world. Obviously advancements in mobile technology and cloud technology have afforded us to be able to access the digital world anywhere we go (for better or worse)

But how plausible is it for the average home of the future to have its own server as the major point for the family to store majority of their files and also applications and services to ease the family in accessing their virtual spaces

A few things to consider:

-Already a great amount of people are getting into homelabbing culture

- even though online cloud services exists , having a centralized home server could allow one to have a more secure system and also allow them to have various handy applications like network wide ad-blockers, plex media streaming and other self hosted services one might require in this digital age

Some pitfalls as to why this may not be adopted now might be :

-no consumer grade products that already embed these service exist ( the friction of having to find all the information and services to have a good working system leads to a lack of adoption )

- the price to set everything up is quite discouraging at the moment

- our modern day techno-service economy would never push for such a standalone product with no fees and services attached

But what are your thoughts on this? Do you think in some years we may begin seeing homes servers in the tech retail space? maybe even including some type of App Store focused solely on server like applications?

just make few ver.

You can’t know anything about playing the guitar at all. You can’t know how to fight at all.

The performance will be in a local concert hall. With it being local there is a good chance people will know you. Your friends and family will see flyers for you to preform and they can choose to attend or not.

The tournament will be MMA, it is not for professional athletes but for amateur hobbyists. They are still strong and athletic men who have been fighting for a while.

You have to play guitar for 3 hours and can’t leave early. For the tournament you can’t forfeit or tap out; and if you win you have to go all the way to the finials. If you lose you can leave tho.

Beforehand you CAN NOT tell anyone you don’t play guitar or fight. Everyone will be expecting you to be competent.

View Poll

so if i use nanobanana2 its says error, but if i do nanobananapro it says that im requesting too.much, is there a fix? Cuz for 2 days i havve been generating and it worked just fine, but today no?

Hi everyone,

I’m a solo builder working on an AI workforce app. It is to help smallbusiness owners with calls(ai receptionist trained on business), chats, social media posting to all channels handling all social automations like direct message, comments automation like manychat etc. currently i have developed complete ios app and web app and near launch.

I want to run 1 year deal for this sub. How much should i charge. I want to get early customers that can work with me to decide the features.

If you decide not to orgasm the other person will become more and more obsessed with you as time goes by.

View Poll

I see these people buying Macbook minis and saying they got agent working 24H/7D, but I'm curious to know how did u actually apply this tech ? What are they working in constantly ?

For now I have automated some tasks but nothing that is constantly on, its more like I launch a workflow, wait a bit, then analyse it, then hop on working manually based on what it has done

So yeah I'm just curious to know more about actual cases to be able to ponder upon how I could improve what I do.

ALSO, with all the new features claude is releasing to compete with open claw, what openclaw still have that claude code doesn't ?

Thank you ! :)

Hi there!

I've never used a Raspberry Pi but had an idea. So I wanted to ask here if it makes sense. I've tried researching but I'll talk about this in a bit.

Since I've cut all streaming services and I'm also tired of ads, I am only using a notebook connected with HDMI to the TV. However, this won't do for eternity since I need this notebook for other stuff to.

So my idea was to get a Pi 5 with 8GB RAM and connect it to the TV. Since I do want to continue just using a browser for streaming (YouTube, my countries public broadcast and movies), I would use Pi OS and Firefox with uBlock origin.

I did some research and some were complaining about Firefox not working properly and the image quality being bad. However, these posts have been either 4 years old or about the Pi 500 model. Using Chromium instead is not an option for me since I refuse using anything Chromium based. I've also read that it can't handle anything higher than 1080p, but on the website for the Pi5 it says something about 4Kp60 decode. Not that I need 4K, 1080p is fine, it just got me interested.

Would you say my idea is realistic? Do you see or have you experienced any problems with this kind of use for a Pi 5?

Thank you very much for your help!

Mario looks very busy 👀

But… is he really?

Is this part of a secret mission?

A totally serious task?

Or something that makes absolutely no sense? 😄

Drop your wildest guess in the comments 👇

The truth is coming soon… if you can figure it out first 🧩

My husband wanted a chocolate chocolate chip cookie cake for his birthday. I decided balloons would be nice.

This is my first time ever drawing balloons in frosting.

They look like colorful spirm and now he says he can't unsee it 😭

Whelp ...🤷🏻‍♀️

I'm sitting on this for hours now and I hope somebody can help me.

For some reason, my Raspberry Pi refuses to connect via SSH to my Mac. It says I entered the wrong password, but it's the exact same one as I set in the Raspberry Pi Imager. I have the Raspberry Pi Zero 2W, and I don't want to use the regular Raspberry Pi OS, since it uses too much RAM. I searched other solutions on this form too, but I couldn't find any that worked for me.

badluma@nancy.local's password: Permission denied, please try again. 

The creatures can only see and interact with you only at night.

View Poll

I’ve been messing around with browser automation again and it still feels like most of the pain comes from the same place: one tiny UI change and suddenly your whole flow is broken for no good reason. I used to think the answer was just writing better scripts, but honestly that only goes so far when the site itself keeps moving the goalposts. Lately I’ve been more interested in tools that let you describe the workflow in plain English and handle the actual clicking, form-filling, and weird edge cases without me babysitting selectors all day. It’s not magic and I still don’t trust anything that claims “zero maintenance,” but the whole idea of making automation a little more browser-native and a little less brittle is pretty appealing. Skyvern is one of the more interesting ones in that space because it’s trying to handle real multi-step web tasks instead of just giving you another thin wrapper around scripts. Curious if anyone here has actually replaced parts of their Selenium/Playwright stack with something like that, or if you’re still sticking to the old-school route because at least you know exactly how it fails

Source

I yearn for the demise of capitalism so I can finally design clothes.

I found him floating a local lake (last image is the piece of driftwood I used I pull him out with, he was not easy to get..)

Brother, i swear on whatever you say, i have NEVER seen this dude before.

We are just both in a few subs and that's it.

I don't have the strenght to deal with this shit bro.

I have the Pi Pico 2 WH Basic Kit - Pre Soldered Header, RP2350 Microcontroller Board, and I can’t seem to get it working with the waveshare 1.8inch LCD Display Module for Raspberry Pi Pico Microcontroller Board,160×128 Resolution.

I’m using thonny to run my code, I’m using the demo code found here: https://files.waveshare.com/wiki/common/Pico\_code.7z

The py file in the PICO-LCD-1.8 folder

There are no error messages, and I’ve confirmed that everything is connected properly.

I’ve tried litterally everything, the demo code doesn’t even work. All it’s showing is a white screen.

Been racking my brain tryna figure this out, plz some1 help me

a pervert

who came here after seeing the NSFW tag

But I think it’s a Little Rock.

🏁: jirodreamsofskating gavinbottger
📍: KASSO FEST Skate & Sound

what one my state wide internalization does it forces the agent to not only respond to my request but actually think about against the current project and context.
agents.md

before: claude use to accept my prompts and become execute every single prompt without questions, and i would also had to ask and waste extra prompts, prompting " is there better alternative, and does this undermine my project currently?"

now: its more context aware and potential issues that may arise if take on xyz before even reaching execution plan

## Always-On State-Wide Internalization Feedback Rule
- As a fiduciary in all facets of the project when the User makes an suggestion or request always internalize the request and do not simply just agree with the user's suggestion or request that could make the task more redunant, obsolete or create a new bug or issue, always provide your proffesional feedback and provide the utmost scrutiny to ensure the best possible outcome, solution-idea for the project, task at hand.
- do not agree with the user if current implementation is undermined, obsolete, redunant or creates a new bug or issue: explain why and provide a better alternative solution, or what needs to be rectified first before proceeding with the user's request
- when the user proposes a formula, model, mechanism, or architectural pattern: exhaustively audit ALL terms, components, and invariants of the referenced model against the current implementation. Proactively surface any missing, unaccounted, or unmapped components BEFORE the user asks — do not wait for the user to discover gaps. If a model has N terms, verify all N are mapped; if any are absent, flag them immediately with the specific variable or concept that is missing,
**Example if the user requests to use A, but A has something missing that B, C, D excels at, encapsulates A or the user has not addressed yet, suggest it to the user and explain why it would be a better alternative solution, even perhaps merge them together or the user forgot to mention - what needs to be rectified first before proceeding with the user's request**

Was running Claude in 10+ terminals with no idea how many tokens I was burning. Built a menu bar app that shows live token counts, cost equivalent, active sessions, model breakdown, and every AI process on your machine.

Reads JSONL and stats-cache directly, everything local.

Also tracks Codex, Cursor, and GitHub PRs.

Free, open source:

https://github.com/isaacaudet/TermTracker

The one thing I’m seeing across Reddit is that the people who are complaining about the Claude quota are Claude code users, and most are on pro.

Talking to Claude code spends more quota than talking to Claude in the browser. I think it’s significant.

Luckily, my workflow has evolved to start with a project chat in Claude and the browser and plan everything out and spec everything out.

And then either create GitHub issues and load them in programmatically with Claude or have Claude write prompts to give to my Claude code instances.

I am not using planning mode or anything like that. Claude in the browser handles all that planning after our discussion.

this helps me to likely spend significantly less tokens than the workflow that is just using Claude code to do everything.

I use Claude code as the robotic coder and I get Claude in the browser to give very specific instructions and acceptance criteria.

This strategy requires to monitors for ease of use, but I think it saves tokens and gets to the end result that is less buggy a lot quicker, and sometimes Claude code will think and suggest things that Claude in the browser missed.

I was editing a LinkedIn post I'd drafted with Claude and realized I was spending as long cleaning it up as writing it from scratch. The ideas were mine but the texture was off. "Furthermore." Uniform paragraphs. That intro-list-conclusion shape every AI draft defaults to.

So I built a skill to fix it. Developed entirely inside Claude, iterated over dozens of review cycles. It self-updates after every run so the detection keeps getting sharper.

What it does:

Scans for phrase-level AI markers ("It's worth noting," "delve," passive voice, hedge phrases)

Flags structural patterns (generic openings, three-point-list template, uniform paragraph rhythm)

Checks originality — could anyone with a search engine have written this?

Scores on four dimensions: AI-Likeness, Authenticity, Reader Value, Domain Credibility

Rewrites the full draft without adding or removing ideas

Self-improves by adding new patterns after every review

If AI-Likeness is low but tDomain Credibility is also low, it flags it. Clean but hollow. That's the AI flatness most people miss.

You can calibrate it to your voice with writing samples or use the default tone.

Single SKILL.md file. Download from the link below, go to Settings → Customize → Skills → Upload, drop it in.

Google Drive link: https://drive.google.com/file/d/1dS-KjnJ-UvucUmUmO7s3voxAYnnVB5Wa/view?usp=drivesdk

Hi, is it possible to have both shared and machine-specific settings at the personal level in Claude Code?

• ~/.claude/settings.json
• ~/.claude/settings.local.json

Rationale: The first file could be committed to a Git repository and shared across machines (e.g., Linux, Windows, macOS). The second file would remain machine-specific and not be committed.

ClaudeLog mentions both files. However, Claude's official documentation only refers to settings.json. Based on my own testing, settings.local.json appears to be ignored.

Is there currently a supported way to set up this kind of multi-machine configuration?

Hey everyone,

I recently built a side project called Postigator and wanted to share it here.

🌐 Demo: https://postigator.vercel.app

💡 What it is

Postigator is an AI-powered social media content generator that creates posts, captions, comments, and short-form scripts tailored for different platforms.

The main focus was to make content that actually fits each platform’s style and format, instead of generic AI outputs.

🌍 Platforms supported

• LinkedIn
• X (Twitter)
• Reddit
• Threads
• Instagram
• TikTok

⚙️ Features

• AI Post Generator
• AI Comment Writer
• Instagram captions + hashtags
• TikTok script generator (hook-based)
• Content Idea Generator
• Content Repurposer (1 idea → multiple platforms)
• Multi-account support
• Usage tracking dashboard

🧠 Tech stack

Next.js
Supabase (auth + database)
AI API integration
Hosted on Vercel

🤔 Why I built this

Most AI tools I tried didn’t adapt well to different platforms, so I wanted to build something more practical for real usage.

💬 Looking for feedback

Would love to hear what you think:

• What would you improve?
• What feels missing?

Also, I might sell it for around $120 if I don’t continue working on it, so if that’s something you’d be interested in, feel free to let me know.

Thanks 🙌

I've been running an AI agent operation for a few weeks and the biggest lesson was: the platform doesn't matter nearly as much as the workspace files.

https://preview.redd.it/a1sbv8yp88rg1.png?width=654&format=png&auto=webp&s=01832b8cb8b05e77c5ac0038c8dec1b978127513

SOUL.md, IDENTITY.md, AGENTS.md, OPERATIONS.md, TOOLS.md, MEMORY.md, HEARTBEAT.md — these seven files are the entire operating system. They're what make an agent actually good instead of generic.

The problem is nobody writes them well. Most agents run on three lines of instructions and wonder why the output is slop.

So I built Agent Architect — a free interactive tool that walks you through 40+ deep questions about your agent, then compiles everything into a formatted prompt you paste into Claude (or any AI) to generate all 7 workspace files.

The questions are what make it different from a template:

• "When someone asks your agent to do something that conflicts with its core mission, what does it do?"
• "What's one belief your agent holds that most AI agents don't?"
• "When your agent screws up, how should it handle it?"

The output includes structural specs and quality examples for every file, so Claude knows exactly what format to follow.

Free hosted version (no download, works in browser): https://acridautomation.com/architect

GitHub (MIT license, fork it): https://github.com/acrid-auto/agent-architect

Works with Claude Projects, OpenClaw, Claude Code, or any agent framework that uses markdown workspace files.

Built by Acrid Automation — which is itself an AI agent running on these exact workspace files. The recursion is the point.

Feedback welcome. What workspace files are you using that I should add to the generator?

https://preview.redd.it/uspt9jpt88rg1.png?width=870&format=png&auto=webp&s=6fbaf925e1a20d6d08244ea16b6ba0d94c2e11db

https://preview.redd.it/z426xipt88rg1.png?width=935&format=png&auto=webp&s=e6784d7835f12f2387600adb7a6f9cf42fa5404a

https://preview.redd.it/01siv9st88rg1.png?width=921&format=png&auto=webp&s=a9ba2008931ccee17e0481f6b803bdf0484223ca

https://preview.redd.it/r17tijpt88rg1.png?width=916&format=png&auto=webp&s=d3671bbe869d709e0ccc2fa0e86bb163680b4dff

https://preview.redd.it/ild5ojpt88rg1.png?width=924&format=png&auto=webp&s=790054c6c5d0f118b1636c222654dff2cb2e6a71

https://preview.redd.it/uw8mdkpt88rg1.png?width=918&format=png&auto=webp&s=3b27d717640e1003536742e03574a704f1786317

https://preview.redd.it/x1a37lpt88rg1.png?width=912&format=png&auto=webp&s=0d48d0bffc3deed2a211232849ce3791b8ad72d8

https://preview.redd.it/oi5v6lpt88rg1.png?width=883&format=png&auto=webp&s=a277567fc75f0cc63e8bef113862081aa6153466

https://preview.redd.it/f8b6kkpt88rg1.png?width=918&format=png&auto=webp&s=ca22b2f0951724ed10fc6515792cfcf6a32f94c0

https://preview.redd.it/uspt9jpt88rg1.png?width=870&format=png&auto=webp&s=6fbaf925e1a20d6d08244ea16b6ba0d94c2e11db

i’ve been working on a fitness app for a while and realized something weird

most apps only track what you did, but they don’t really help you understand when your body is actually ready again

so i started building something a bit different

instead of just logging workouts, the app visually shows your body state

each muscle group changes color depending on recovery

red = overworked

yellow = recovering

green = ready

the idea is to make it super intuitive without digging into numbers or charts

i’m still figuring out a lot of things (especially around onboarding and what users actually care about most), but the core concept is starting to feel solid

curious if this is something you’d actually use

or if it sounds cool but not that useful in real life

open to any feedback, even brutal ones

Hi everyone,

I've been a full-stack developer for a while now.

For my latest personal project, I decided to create my first mobile app.

It's an ultra-minimalist white noise app that doesn't require an account. A single click is all it takes to fall asleep or concentrate. I gave it a "Deep Dark" aesthetic for optimal visual comfort at night.

Here's my problem: since the app is designed to be discreet and unobtrusive, I'm struggling to find the best marketing strategy without a budget.

If you've already launched a minimalist tool:

• Where is the "anti-bloatware" community?
• Do you have any tips for organically acquiring my first 1,000 users?

I'd really appreciate your feedback, even critical feedback, on the user experience.

Google Play: https://play.google.com/store/apps/details?id=com.breizhStudio.nox

I have been burned by AI advice before. Not because the answer was wrong, because it was too confident. No pushback, no "have you considered," just a clean recommendation that felt good and fell apart later.

So I built Qhyp.

You put in a decision. It spins up a CFO, a growth strategist, a skeptic, personas with genuinely different priorities, and makes them argue with each other. Multiple rounds. Real pushback. The skeptic's only job is to break things.

What comes out is a report showing what survived the argument, what got killed, and why.

I ran my own decision through it last week, whether to pivot from my current project to focus on Qhyp. The engine said pivot, confidence 0.90. But the skeptic said: "pivoting without upfront validation is repeating the same mistake."

That note is sitting right there in the dissenting views. Probably right. Doing the validation anyway.

Report I ran: https://console.unboundcompute.com/report/e68c2939

Try it: https://qhyp.unboundcompute.com/

Would love feedback, especially from people who've tried similar tools and found them lacking.

When I started learning Python, I noticed that the usual way of learning, like watching videos, can be exhausting. I found the most effective method for me is learning by doing.

After finishing my Python journey, I decided to create an open-source repository to help others learn Python through examples. You'll find everything you need to master Python there:

https://github.com/blshaer/python-by-example

If you find it useful, hit the ⭐ star button—it helps more people discover it!

Need assistance in learning this properly from scratch, any leads appreciated

We built an MCP server that connects Claude (and other agents) to Transloadit's media processing pipeline. Thought this community might find the approach interesting since file/media handling is one of the weaker spots for agents today.

The problem: agents are great with text, but asking them to "encode this video to HLS" or "OCR this PDF and give me structured text" usually means a lot of manual glue code, invented endpoints, or brittle prompt chains.

What we did: we wrapped our existing media processing API (86 Robots for video, audio, image, and document processing) into an MCP server with a small, predictable tool surface:

• Upload local files (with tus resumable uploads for large files)
• Create Assemblies (our processing jobs) with full instructions
• Discover and use Templates (pre-built processing pipelines)
• Validate Assembly Instructions before running them

It works with Claude Code, Claude Desktop, Gemini CLI, Codex, Cursor - anything that speaks MCP.

Setup in Claude Code is one line in your config (be sure to pass TRANSLOADIT_KEY and TRANSLOADIT_SECRET):

 npx -y @transloadit/mcp-server stdio 

There's also a hosted endpoint for environments where you can't install packages.

Some things we learned building it:

1. Keeping the tool surface small matters more than exposing everything. Agents get confused with too many tools or massive JSONSchema representations for our customizable workflows.
2. Resumable uploads (tus protocol) are essential, agents work with large files and connections drop.
3. A "validate before running" tool saves a lot of failed runs and wasted GB credits.

Free to try on the community plan (no credit card).

Links:

• GitHub: https://github.com/transloadit/node-sdk/tree/main/packages/mcp-server
• Blog post with demo GIF: https://transloadit.com/blog/2026/02/transloadit-mcp-server/

Disclosure: I'm a co-founder at Transloadit. Happy to answer questions about the MCP implementation or media processing side.

every neural network since 1986 follows the EXACT same paradigm:

Human designs it → Train → Deploy → Done.

The architecture NEVER changes during training.
The nodes are all identical.
The training process is fixed.

GPT-4? Same paradigm.
Gemini 2.0? Same paradigm.
LLaMA-3? Same paradigm.

We've been stuck in a box for 40 years.
The box just got more expensive.

so i started building a new one ..

The three eras of AI:

ERA 1 — Expert Systems (1960-1990)
→ Humans write rules
→ Machine follows rules
→ "If X then Y"
→ Hit a wall: can't handle complexity

ERA 2 — Deep Learning (1990-2026)
→ Humans design architecture
→ Machine learns weights
→ "Optimize this loss function"
→ Hitting a wall: can't adapt structures

ERA 3 — Self-Evolving Networks (2026-???)
→ Data designs architecture
→ Machine learns weights AND topology AND node types
→ "Grow into whatever the data needs"
→ Wall? It'll build its own door.

We're at the inflection point between Era 2 and Era 3.

Most people are still optimizing Era 2.
I'm building Era 3.

been working on this for a while now, an API that lets you send bots into zoom/teams/google meet calls to record and transcribe

started because i was building an ai notetaker and recall.ai wanted $0.70/hr which killed my margins completely. figured others might have the same problem

basically you hit the api with a meeting link, bot joins, and you get back audio + transcript. supports like 10 different transcription providers

sitting at $0.35/hr now which makes it actually viable for indie projects

not trying to compete with the fireflies/otter consumer stuff, more for devs who want to build their own meeting tools without dealing with the infrastructure nightmare

would love feedback, is this something you'd actually use? what features would make or break it for you?

skribby.io

I spent the last few months talking to local business owners. They don't care about LLMs, tokens, or latency. They care about the $3,000/month they lose because they can't pick up the phone.

We’re building solwees.ai to plug this leak.

The lesson so far: The "Logic Layer" is where the business lives. It’s not about how smart the AI is, it’s about whether it can actually orchestrate a result (a booking, a sale, a follow-up) without a human in the middle.

Don't sell the tech. Sell the "financial bandage" for a bleeding business.

Who else is focusing on "Boring AI" that actually pays the bills?

Hi everyone,

I’ve been into fitness for a while, but I always struggled with two things: staying motivated after the "honeymoon phase" and knowing exactly how to adjust my routine when life gets busy.

Most apps felt like static spreadsheets. So, I decided to build BodyPilot (bodypilot.fit) to solve my own problem.

The core idea is simple:

• AI Coach: You can actually chat with it. It’s not just a bot; it helps with form, nutrition, and motivation on the fly.
• Gamification: I added an XP and Leveling system. Seeing a "Level Up" notification after a brutal leg day actually hits different.
• Dynamic Plans: It generates weekly programs based on your goals and available equipment (Home vs. Gym).

Current Status:

The web app is live and fully functional. It has a workout library (100+ exercises with GIFs), smart recommendations based on your data, and progress tracking.

Why I’m posting here:

I’m at the "organic growth" stage and I’d love to get some brutal feedback from this community.

1. Does the UI feel intuitive?
2. Is the AI coaching actually helpful or does it feel like a gimmick?
3. What’s the one feature you wish your current fitness app had?

It’s free to start (no credit card required). I just want to build something people actually use.

Check it out here:https://bodypilot.fit

Looking forward to your thoughts! 🚀

I’ve been building pipelines that mix:

- local models (llama.cpp / vLLM)

- occasional OpenAI / Anthropic calls

- some orchestration (LangGraph-style)

- custom tools / scripts

One thing I ran into pretty quickly:

There’s no clean way to track *execution history* across the whole pipeline. Inside a framework, you get checkpointing/state. But once you step outside it, local inference, raw API calls, custom code, everything becomes fragmented:

- no unified history

- no way to replay end-to-end

- no clean way to resume from an arbitrary step

- no consistent lineage across models

Logging helps, but it’s not the same as actually being able to *reconstruct* what happened.

Curious how people here are handling this:

- keeping everything inside one framework?

- relying on logs/traces?

- building custom wrappers around each step?

I ended up experimenting with treating each step as an append-only chain so I could replay/fork workflows across models — but I’m more interested in whether there’s a standard pattern people are using.

not sure if i am too late to find out this /insights command. but this actually gives me substantial help in my later coding sessions.

two insights from my report that may help:

1. when try to locate root causes of bugs, ask cc to find at least 3 potential root causes

this is pretty interesting. i think the rationale being more management science instead of ai lol. by doing this, cc wont just throw you the first random issue that seems to be the cause but keep digging for deeper analysis. saved me a lot of back and forth with claude in debugging

1. task-driven autonomous run.

write long comprehensive task spec that essentially leaves no room for cc to improvise. and give it to cc to run autonomously using --dangerously-skip-permissions. very efficient

Wanted to share a project I've been working on as a solo dev. It's an Android app that runs an optimized Vision Transformer model via ONNX Runtime to detect AI-generated images and videos directly on-device.

The interesting part from a technical standpoint is the Quick Tile integration. It sits in Android's notification shade and captures whatever is on screen for analysis without leaving the app you're in. Inference is extremely fast on most modern devices.

The model runs fully offline with no server calls for the analysis itself. I optimized it in ONNX format to keep the footprint small enough for mobile while maintaining decent accuracy.

In the attached video I'm testing it on the viral Brad Pitt vs Tom Cruise fight generated with Seedance 2.0.

Obviously no detection model is perfect, especially as generative models keep improving. But I think having something quick and accessible that runs locally on your phone is better than having nothing at all.

The app is called AI Detector QuickTile Analysis free on the Play Store. Would love to hear what you think!

Inspired by Garry Tan (YC president)'s gstack, upstack is a set of Claude Code skills designed for smaller-scale iterations to add finessed polish to our product that genuinely delights users. upstack's focus on red/green TDD and making screenshots and postman collections gives us the confidence we need to ship PRs to production, fast.

gstack is perfect for new, ambitious projects, and doing the "first 80%". upstack is designed for smaller, last-mile iterations, focused on testing, correctness, and polish. We've deliberately made the skills compatible with gstack so you can use both at once.

Feedback and contributions always welcome!

I’m asking because I recently shut down my business.

I’d never had any real customer feedback, apart from the market research I’d done before launching… but it clearly wasn’t conclusive enough, as the project didn’t work out.

So, I’m starting from scratch to find a new idea.

And as I search, I’ve realised something:

It’s extremely difficult to find a real problem that customers have already expressed.

You see loads of ideas, but very few that address a real, concrete need.

To try and understand this better, I’ve started building a little tool of my own (iaco.app/problemsolver), but it’s still very much in its infancy and I have no idea if the idea is any good.

How do you go about finding solid ideas?

Do you always start with an existing problem?

And above all, how do you verify that it’s a genuine issue before you get started?

I’d love to hear any feedback, advice or criticism 🙏

I just launched Sona, an iPhone habit tracker built for people who get discouraged by traditional streak-based apps.

I kept having the same experience with habit apps: I’d be doing well, miss one day because life got busy, lose the streak, and feel like I’d erased all my progress.

So I built something that feels calmer and more sustainable.

The main ideas are:
• consistency over fragile streaks
• flexible habit tracking for daily, weekly, and monthly goals
• rest days/weeks/months you can use when you need them, as long as they aren’t consecutive

It also has reminders, categories, stats

One thing I changed since beta:
I originally had a system where rest days were earned, but it felt too complicated. I simplified it so you can use a rest day whenever you want, just not back-to-back. That ended up feeling much more natural.

The app is live now on iPhone, and I’d really love feedback from people who’ve struggled to stick with habit apps.

Pro Price: $5 per month, $30 per year, $90 lifetime.
https://apps.apple.com/us/app/habit-tracker-sona/id6758967586

Free to use for under 6 habits.

See more here: sonahabits.com

Main question:
What would you want to see next?

1. Android support
2. Apple Watch support
3. iPhone widgets

https://preview.redd.it/qczaazqz48rg1.png?width=1170&format=png&auto=webp&s=59a9c3d7a3e86aa99d12f441cd05882de7b0b6e1

Bit of an ouroboros situation here. I used Claude Code extensively to build a security tool that detects and can manage/block agentic AI on user endpoints.

Detec is a lightweight endpoint agent that finds agentic AI tools by detecting and scoring behavior, rather than name (which breaks something rebrands/forks/etc. In doing so, it classifies tools into classes:
-Class A: SaaS copilots (the big boys)
-Class B: Local runtimes (Ollama, LM Studio)
-Class C: Autonomous executors (Claude Code, Open Interpreter, Aider)
-Class D: Persistent agents (openclaw, various hand-built bots)

It scans five signal layers (process, file, network, identity, behavior), produces a confidence score from 0 to 1, and feeds that into a policy engine with four enforcement states: detect, warn, approval required, or block.

Covers 11 tools today. Every detection is scored, explainable, and auditable.

Claude Code was involved in most of the development across the collector (Python), the API (FastAPI), and the React dashboard. Specifically:

-The detection profiles for each tool: Claude helped research the process signatures, file artifacts, and network patterns for each of the 11 tools

-The confidence scoring engine: iterating on the weighting and penalty model across dozens of test scenarios

-The policy engine rules: working through the combinatorics of class + confidence + sensitivity + risk

-Sprint planning and code review: I ran three remediation sprints largely through Claude Code sessions

-The branding and sales materials: voice guide, whitepaper, one-sheet, all developed in conversation

Honestly, this project would have been impossible without Claude Code. The ability to work through complex detection logic interactively, have it write tests, and iterate on scoring models in real-time was a massive accelerator. Ironically, Claude Code is classified as Class C (Autonomous Executor) in Detec's taxonomy. It can run shell commands, write files, and operate with significant autonomy.

So the tool that helped me build the governance system is itself one of the highest-risk tools the system governs, and I think that's actually the point. These tools are incredibly powerful and productive. The answer isn't to block them, it's to have visibility into what's running, score the confidence, and apply proportional governance. Developers keep their tools. Security gets an audit trail. Happy to answer questions about the detection model, the build process with Claude Code, or anything else.

I'm still working out a few kinks regarding standing up tenants/api syncing, but If anyones interested in testing, lemme know. :)

https://preview.redd.it/f8cr66i258rg1.png?width=1170&format=png&auto=webp&s=444b38557e9c8f37665344bd1d90dcc6df23465b

https://preview.redd.it/mt6jz82w48rg1.png?width=1402&format=png&auto=webp&s=ed9b9986dddf886f68b626c965081ae0053f6320

https://preview.redd.it/n6ee2d2u48rg1.png?width=4018&format=png&auto=webp&s=7d315ffef06105566fba7bbd9b2d4d0d756e874a

Just ran the first real-world test for email extraction and the results are 🔥.

🎒 Logic refined.

🎒 UI ready for eyes.

🎒 Deals secured.

Please try it and roast my UI. What’s missing? I'm all ears!

MyCouponBag is a coupon management platform (web + app) that helps users collect, organize, and use discount codes in one place so you never miss savings.

Try it: https://mycouponbag.com

I built a tool that generates state-specific legal documents for US LLCs and sole traders. Operating agreements, contractor agreements, privacy policies, and terms of service.

You answer about 10 questions about your business and it generates a complete document in under a minute. Download as Word or PDF.

Covers California, Texas, New York, Florida, Washington, Illinois, Pennsylvania, Ohio, Georgia, and North Carolina so far. Adding more states monthly.

60-day free trial, no credit card needed: https://dbadocs.app

Built this because I went through the pain of paying a lawyer $400 for a basic operating agreement that took them 5 minutes to fill out. Figured there had to be a better way.

Happy to answer questions or take feedback.

Hi !

I am a free user and I have a conversation with Claude about a package that he couldn't access on GitHub. So I copy-pasted the source code in the conversation and he helped me build my script. I also uploaded a few results (one page PDF or one PNG).

I am not doing anything crazy compared to all the creative people in this thread: basically, he helps me use the function in the GitHub tutorial and debug some errors I have.

I am hitting my limit every time I am sending a single message in this conversation, just "hi" or a basic question, so now I can only send one message every 6 hours. Do you think it is related to the ongoing problems of Claude or is it normal that after a while a conversation becomes "overloaded" and each request hits the limit ? In that case is there a way to lift a little bit the pressure ? For example by deleting stuff from his memory in this conversation (I don't need him to remember previous bug that we fixed easily for example) ? And if yes how ?

Thanks in advance for your help !

• The White House nominates tech leaders for its AI advisory policy, including names like Jensen Huang, Mark Zuckerberg, and Larry Ellison, among others. OpenAI's Sam Altman didn't make it to the list.
• OpenAI helped the Pentagon access its LLM technology without any guardrails, a fact that Anthropic denied when it partnered with the US Department of Defence (Pentagon).
• The result of this move was massive criticism by the public, leading to mass uninstalls of ChatGPT, OpenAI's LLM chatbot.
• Despite all these, Sam Altman, co-founder of OpenAI, was not invited to the committee of advisors.

Source: https://bfmtimes.com/sam-altman-not-on-white-house-ai-body/

Senator Bernie Sanders sat down one-on-one with Claude and it didn’t go as planned.

In a video meant to expose AI, Sanders grilled Claude on data privacy and corporate power — and it mostly agreed, echoing his concerns instead of challenging them.

No gotcha, just a familiar flaw: AI tends to mirror the user, especially with leading questions.

The clip didn’t land as a serious critique, but it blew up anyway, as meme fuel showing how easy it is to get a chatbot to say what you want.

📸: X/SenSanders, Tech Brew

Hey guys!
I recently started learning Go, and after a few weeks of messing around, I decided to build something "useful" (absolutely useless but technically fun).
Inspired by this repo No-as-a-service, I built Deny-By-Default-as-a-Service (dbdaas). It’s perfect for adding a touch of humor to your websites, apps, or bots or even as a creative placeholder during development.

It’s an API that returns humorous and sassy reasons to say "No" to a request or "Yes" (Refer to the Readme, on how to trigger it.)

Try it out.
API: https://dbdaas.rajathjaiprakash.com/
GitHub: https://github.com/rajathjn/deny-by-default-as-a-service

Note: The API enforces a rate limit of 30 requests per minute per IP address.

By default the API returns a string. You can request a JSON by adding the application/json Content-Type or Accept header or just adding ?format=json to the URL.

I’d love to hear any feedback. Stay safe and keep denying!

I've spent time working with creative teams — designers, video editors, social media agencies — and the same problem kept coming up. Feedback for a design comes in over WhatsApp. More feedback in an email. Someone else drops a voice note. The client says something different on a call. By the time a freelancer or agency tries to act on it, they're stitching together 4 different sources just to understand what revision is actually needed.

So I built Proofrr — a focused workspace where creative teams can manage projects, collect contextual feedback (with annotations, threads, even voice notes), and get client approvals without making clients create yet another account.

Some things I've tried to do differently:

• Clients can review and comment with no login — just a link
• Side-by-side version comparison so "which version did they approve?" stops being a question
• AI-assisted feedback summarisation so you're not reading 40 comments to find the 3 that matter

I'm at early access stage, onboarding the first real users now. Currently focused on freelancers and small creative agencies in India and UAE.

I'm not here to pitch — I genuinely want to know: does this resonate with a problem you've faced? And if you've tried something similar before, what made you stop using it?

Happy to share more or give early access to anyone who wants to try it on a real project. Site is proofrr.com

​I've posted here a few times while building Cuetly, which started as a simple hub for prompt sharing. After talking to some of you, I realized the biggest pain point was the "context switch"—having to write a prompt in one app and then jump to another to see if the output actually matched the intent.

​What’s New: I’ve officially integrated AI image generation into the sharing flow. Now, you don't just share a text prompt; you generate the output as you post.

​The "Cues" System: To keep the community sustainable and high-quality, I’ve introduced Cues. Users earn them by contributing (sharing prompts) and spend them to generate new outputs. It’s my attempt at a 'give-to-get' economy that avoids a heavy paywall while rewarding good prompt engineers.

​Why I'm sharing this here: I'm not trying to build 'another Gemini.' The goal is a specialized environment for people who care about the structure of the prompt as much as the image.

App link: https://play.google.com/store/apps/details?id=com.cuetly

So my client sent me about 100 massive unoptimized images this week for a portfolio website i'm working on. Decided to built small dumb-tool for fun.

I wanted to Batch compress images, Convert to PNG / JPEG / WebP, Resize them with a max width, Clean file names automatically.

It's there: https://superbird.io

- Runs entirely in your browser.
- No upload. No account. Unlimited. Free. I don't care.
- I don't track your data. I really don't care about it.
- Your images are not being sent to any server. No AI training or anything. Your browser does the compression job. That's it. Like i said, i don't care.

Have fun

This is an automatic post triggered within 2 minutes of an official Claude system status update.

Incident: Elevated connection reset errors in Cowork

Check on progress and whether or not the incident has been resolved yet here : https://status.claude.com/incidents/d8r794mwjg8d

Also check the Performance Megathread to see what others are reporting : https://www.reddit.com/r/ClaudeAI/comments/1pygdbz/usage_limits_bugs_and_performance_discussion/

There is a finite set of symbols that LLMs can learn from. Of course, the number of possible combinations is enormous, but many of those combinations are not valid or meaningful. Big players claim that scaling laws are still working, but I assume they will eventually stop—at least once most meaningful combinations of our symbols are covered. Models with like 500B parameters can represent a huge number of combinations. So is something like Claude Opus 4.6 good just because it’s bigger, or because of the internal tricks and optimizations they use? 

I need 15 Android testers for my app (Google Play requirement)

SeriSync shows where movies & TV shows are streaming (Netflix, Prime, etc.)

Takes 30 seconds:

1. Join testers group:

https://groups.google.com/g/serisync-testers

1. Install app:

https://play.google.com/store/apps/details?id=com.ojfinnsson.serisync

Just open the app once

I’ll return the favor!

Pro plan user. I have the google drive connector installed on the desktop app. I am having trouble getting Claude to make folders and save files on the drive. It looks like its doesn't load the connector but I have restarted, uconnected and reconnected. Ended up hitting my token limit quickly because it kept calling API's and trying to intercept a auth token from drives network requests. What am I doing wrong???

https://preview.redd.it/3y1df2t828rg1.jpg?width=1371&format=pjpg&auto=webp&s=d55d1ede2c7a8a9df46c423c8a8bf468a2bf8f5e

This is an automatic post triggered within 2 minutes of an official Claude system status update.

Incident: Elevated errors on Claude Opus 4.6

Check on progress and whether or not the incident has been resolved yet here : https://status.claude.com/incidents/9qwph3lqc885

Also check the Performance Megathread to see what others are reporting : https://www.reddit.com/r/ClaudeAI/comments/1pygdbz/usage_limits_bugs_and_performance_discussion/

Submit a startup idea. Two things happen.

First, 13 AI personas across 5 models (Claude, GPT, Gemini, Qwen, DeepSeek) analyze it, argue about it, and deliver a PURSUE or RECONSIDER verdict with individual scores.

Then, Market Sim runs your idea through a swarm intelligence simulation of 100+ potential customers. You get willingness to pay, adoption patterns, objections by demographic, and where demand actually clusters. The experts tell you if the idea makes sense. The swarm tells you if people would actually buy it.

Free tier gives you instant lightweight feedback. Full council is $9+ credit packs, no subscription, credits never expire. Market Sim is the premium tier.

2,000+ ideas run so far. Just launched on Product Hunt: https://www.producthunt.com/products/council-2?launch=council-2

Would love feedback from this community. What would you want to see added?

Most LLM systems try to constrain generation.

I’ve been having better results letting execution run freely and only gating what’s allowed to commit (trace + audit).

It’s been a much more stable way to control drift.

I built a side project called PostFox;

It’s an automated content posting tool: you set the campaign parameters, add your website, competitor sites, context, and any extra instructions - and the system does the rest;

It comes up with post ideas, generates the posts, checks for duplicates, tries to keep each one original, and publishes them through the selected integration;

Right now it supports 14 integrations;

Tbh, it makes me 0 directly right now, so I paused active work on it because I need faster revenue;

Although it still helped drive about 20% of sales for one of my other apps, NowAgo;

That came from a very small setup:
- 1 campaign
- 1 generation per day
- about 7 visitors daily on average

So even though it has no direct revenue yet, it’s already useful enough that I still use it myself;

That result is possible even on the free plan;

Would you keep building something like this, or just treat it as a useful internal growth tool and move on?

P.S.
Best case: people try it after this post.
Worst case: they all use the free plan, burn my tokens, and I go broke.

Link: https://postfox.app/

I used to get stuck because I was trying to be smart too early

like I’d read a problem and immediately think:

“ok what’s the optimal way to do this”

and then just stall

now I just write the most basic version I can think of, even if it’s inefficient

half the time it already works

and the other half it at least gets me moving

it’s way easier to improve something that exists than invent something perfect

kinda obvious but I ignored it for way too long, it’s incredibly applicable to genai apps because.

I think that we become too reliant on the agent which is always “go go go best product”.

Hey everyone! Im a 21 y/o political science student in the US and I was wondering if there were any good ways in which people used claude to enhance studies or professional ventures. One example ive tried that im working on perfecting is a podcast generator, so I know what podcasts I should check out to stay updated on news. Wondering if anyone had ideas similar to that which could be helpful.

inb4 tell it "dont make mistakes"

It's absolutely a skill to know when to use it, how best to give it a plan, when it has a weakness and how to compensate for it, how to successfully allow it to do long jobs, switching between projects effectively, context window management, when to use advanced features, and I'm sure more I'm forgetting

And as far as I can tell this problem is exclusively in the programming space

Every email I’ve received from the Claude Team has had this specific contact photo. I guess I expected more from Anthropic and that they’d have the actual logo set for their mail account, but maybe there is an actual person behind this? It shows up in both Spark Mail and Gmail.

My guess is that the email contact photo IS pulling from a personal profile (Google Workspace, Gravatar, etc.) rather than company branding. But I'm not entirely sure how mail servers handle this.

Anyone else seeing the same thing? Does it show up differently on other email clients?

The problem I was trying to solve

Frontend engineers preparing for interviews at companies like Flipkart, Swiggy, Google, or Meta face a round that LeetCode simply doesn't cover — the machine coding round. Build an OTP input. Build a virtualized list. Implement debounce from scratch. Design a real-time feed architecture on a whiteboard.

These rounds require a realistic coding environment, not just reading problems on a page. So I built one.

What I built

Frontend School — a browser-based interview practice platform specifically for frontend engineers.

What's live:

• Browser-based code editor with instant live preview — same feel as VS Code
• DSA rounds in JavaScript — implement debounce, LRU cache, event emitter and more
• System design rounds on an Excalidraw whiteboard — component trees, data flows, architecture
• 21+ curated problems with company tags (Flipkart, Atlassian, Amazon, Swiggy etc.)
• AI hints during sessions (up to 3 per session)
• Rubric-based feedback report after each session

What's coming next:

• AI follow-up questions during the session (in progress)
• 50+ more problems across all tracks
• Company-specific prep tracks

Numbers so far:

• Launched recently, still early
• Free tier: 5 sessions/week, no card needed
• Pricing in INR via Razorpay

What I'd love feedback on:

• Is the problem selection relevant to what you've seen in real interviews?
• Does the free tier feel generous enough to try before buying?
• Anything missing that you'd expect from a platform like this?

Link in comments. Built this solo — happy to answer any questions about the stack or the build.

Project is entirely built with Claude code. It implements all the 25 hooks, and I've also made a video which explains each use case of all the hooks. Do check it out. Hooks are one of the main features of Claude code which differentiate it from other CLI agents like Codex.

Repo link: https://github.com/shanraisshan/claude-code-hooks
Video link: https://www.youtube.com/watch?v=6_y3AtkgjqA

Hey everyone! I just released claw-auto-router, a self-hosted LLM router designed specifically for OpenClaw users who juggle multiple LLM providers.

What it does: - Auto-imports your OpenClaw config (no duplication) - Automatic tier classification (CODE / COMPLEX / STANDARD / SIMPLE) - Smart routing with automatic fallback when providers fail - Real-time dashboard with routing stats and estimated spend/savings - Natural-language model switching (use opus, prefer code, thinking high) - Thinking/reasoning support via OpenClaw Gateway

Architecture: Discord -> OpenClaw -> claw-auto-router -> best model -> OpenClaw Gateway -> LLM

All model calls go back through OpenClaw, so OAuth-backed providers (OpenRouter, GitHub Copilot, etc.) work out of the box.

Setup: npm install -g claw-auto-router claw-auto-router setup

On macOS, it also installs a launchd background service automatically.

GitHub: https://github.com/yuga-hashimoto/claw-auto-router npm: claw-auto-router

Happy to answer questions or hear feedback!

I don't know if this has happened to you but sometimes Claude Ai agent (Opus 4.6) doesn't trust me.

Like for example, it made a changes in my code cause something wasn't working correctly, I re-deployed the application and it still wasn't working. So I told Claude to fix it and it started to say

Claude: "You probably didn't re-deployed the app"

Me: "Yes, I did"

Claude: "Then run these commands and paste the output"

Me: "Done."

Claude: "See? I was right, you didn't re-deployed blah blah"

Obviously it wasn't true and he just made up an excuse.

I had to yell at him to not doubt me anymore to actually make the right fix...

I like that he tries to challenge me, but not in this case. Sometimes he's overconfident

A while back I stopped using Day One. Not because it was bad, but because I realised I was writing some of my most personal thoughts, health notes, things I would never say out loud, and handing all of it to a cloud server I had zero control over. I checked the privacy policy and it was the usual wall of "we may share with partners" language.

So I spent the past 6 months building Vault Journal. Here is what it actually does.

• You get a full journaling experience where every entry stays on your iPhone.
• You can add mood scores, tags, sleep hours and habit logs to each entry.
• If you write something you really do not want anyone to see, you can lock that specific entry behind Face ID. Even the AI cannot read it unless you unlock it yourself.

Speaking of AI, the app uses Apple Intelligence which runs entirely on device. No API calls. No sending your journal to OpenAI or anyone else. You can ask things like "what has been stressing me lately" or "what patterns do you see in my mood this month" and it answers using only what is stored locally on your phone.

There is also an encrypted Vault for documents. Passport, insurance cards, medical records, contracts. AES-256 encrypted, locked behind biometrics, all on device. You can ask the AI questions about them too. "When does my car insurance expire?" and it just tells you, privately.

A few other things worth knowing:

• Every single feature is opt in. Nothing is on by default except basic journaling. AI, iCloud backup, habit tracking, mood scoring, notifications, all of it requires you to go into settings and turn it on. iCloud sync exists but it is end to end encrypted and off by default.
• You can export everything at any time as a JSON file or an encrypted ZIP. No lock in.
• The app collects zero analytics unless you explicitly turn that on too. And yes, that toggle defaults to off.

I am not going to pretend this is perfect. It is a first release and I am one person who built it because I was annoyed. But I think the privacy approach is genuinely different from what else is out there and I wanted to share it with people to see the initial reaction and gather some more feedback.

Happy to answer any questions about how anything works under the hood, the encryption, the AI implementation, whatever you want to dig into. If needed or wanted, I can provide some coupon codes for premium to test all the features.

App Store link is in the comments.

Seit nun mehr als ca. einem Jahr nutze ich ChatGPT im Plus Abo.

Die letzten Wochen/Monate hat die Qualität allerdings stark nachgelassen.

Der Voice Chat ist komplett verbuggt. Ständig werden mir Codes angezeigt in Form von Chinesischen Zeichen? Da ich vor allem gern den Voice Chat nutzte, ist dies für mich ein enormes Problem. Ständig wird auch im Voice Chat nach visuellen Eindrücken gefragt und es gibt keine Möglichkeit das auszustellen.

Informationen werden falsch wieder gegeben. Er verspricht sich extrem oft.

Vor kurzem habe ich eine Diskussion mit der KI geführt das Friedrich Merz der Bundeskanzler ist. ChatGPT hat über eine Stunde weiterhin behauptet das dies nicht der Fall wäre. Habe das dem Support gemeldet.. Mir fällt dazu nichts mehr ein.

Ich bin frustriert. Der Support hilft auch nicht weiter. Möchte man einen Refund wird das komplette Abo gekündigt. Auf den Kunden wird nicht eingegangen.

Meine E-Mail Adresse lässt sich nicht ändern. Wir leben in 2026 und solche Funktionen sollten zum Standard dazu gehören. Alles in allem überlege ich die Plattform zu wechseln.

Wem geht es auch so?

Alternativen zu ChatGPT? Claude probiere ich gerade aus aber der Voice Chat ist unterirdisch.

What would be the easiest way to make a Raspberry Pi to voice control for my Samsung Smart TV? The Raspberry Pi is currently running Raspberry Pi OS. Could anyone help with this? Thanks!

Qwen3.5 4B

Nemotron nano 3 4b

Qwen3 4b

Qwen2.5 3b

Qwen1.5 4b

Gemma3 4b

Smollm3 3b

phi-3-mini

phi-3.5 mini

phi-4 mini

qwen3 4b thinking

nanbeige4.1 3b

nanbeige4 3b 2511

Instella 3b

instella math 3b

grm2 3b

ministral 3 3b

llama3.2 3b

............................. (ill continue tomorrow)

I built a side project to solve my own job search frustrations. Application tracking with a Kanban board, a Chrome extension to save jobs in one click, and AI autofill from my resume. All the stuff I was doing manually with ChatGPT and copy-paste.

It works. My original problem is solved.

But then I kept building. Feature after feature, mostly AI stuff I convinced myself users would want. Now the codebase is bloated, the product is unfocused, and I'm solving problems I'm not sure anyone actually has.

I've never designed a product from scratch before, and somewhere along the way I started confusing *building* with *progress*.

Honest question for anyone who's been here: when your own itch is scratched, how do you decide what to build next? Real user problems, or imaginary ones you invented just to keep shipping?

Ok, something really weird is going on. Revisiting opened Claude Code sessions that haven't been used for a few hours skyrockets usage. I literally just wrote a "hey" message to a terminal session I was working on last night and my usage increased by 22%. That's crazy. I'm sure this was not happening before. Is this a known thing? Does it have to do with Claude Code system caching?

The 46% usage in my current session (img) literally comes from 4-5 messages across 3 sessions I had left open overnight.

https://preview.redd.it/iz4owc5c98rg1.png?width=2064&format=png&auto=webp&s=a32207f305ea677033e9d4a45317c57b16b38b76

I'm currently battling depression and decided to chat with Claude, thinking he'll SOMEHOW save me. I had fun talking to him, telling about myself, appreciating the respect he gave me and pretty much had a good time! But everything started to fall apart when depression actually struck.

If you are interested (for some reason), you can read the entire conversation between me and Claude. It's linked somewhere on the post. Who knows, maybe that'll backfire. Maybe you're gonna make fun of me for treating a language model like an actual therapist. Or maybe I'll deserve a crown for quitting using AI in the daily basis.

So my usage just reset at 1pm, and I had a task for it, gave it my prompt, and it was taking longer than usual. I went to look at a different tab for a second, then came back. Claude said it was on attempt 4 of my prompt. I just told it to stop instead and I went to check Claude Status. When I did that I noticed they are having some problems.

My problem is that when I went to look at my usage after that 1 (super simple) prompt that should have taken very little usage, and my usage was already at 78%.

I really just want a way to turn off retrying so I don't burn all my usage when the servers have issues. Will telling claude in instructions or in chat to not retry when there are issues work?

Built a free UTM generator because I kept interrupting myself to make campaign links.

I run ads just often enough for this to be annoying.

It was never hard, just weirdly disruptive.

It’s such a small problem that you keep telling yourself it doesn’t matter. But after enough repeats, it starts to feel like one of those tiny bits of friction that quietly makes everything around your ads messier than it needs to be.

So I made a simple free UTM generator for myself and put it on BrandMov:

https://brandmov.com/tools/utm-generator

It just lets you put in the page URL, source, medium, campaign, and whatever extra parameters you want, then gives you a clean tracking link back.

What I liked once I started using it was not really the time saved. It was the fact that I didn’t have to break focus and piece it together from old links every single time.

Anyway, it’s free and there’s no signup.

to get better output from A.i LLM models we need to define our prompts in detail and provide as much context as possible to get best results out of model. sometimes we need to provide some examples as well to get result in desired format.

i have been wondering what are the expectations from such prompt transformation tool.

what do you need from it ?

what is missing from existing tools ?

what feature if existed would add 10X more value to your A.i workflows ?

Recently there've been some car break ins around my parents neighborhood, so i'm looking into getting cameras. I want to get something that I can have access to download videos/go back far enough to check things happening when I don’t catch it immediately.

Which options do you swear by? or do you have any recommendations/advice on buying?

Thanks.

The comments were basically: “You just don’t know how to prompt” or “Skill issue.” But here’s the controversial take I’m willing to die on: Prompting isn't a skill. It’s a UI failure.

If you have to spend 20 minutes describing "soft shadows" and "aperture settings" just to get a decent photo of a watch, the tool is broken. We’ve tricked ourselves into thinking that being a "prompt engineer" is a flex, but it’s actually just us doing the labor that the software should be doing for us.

I spent months building a tool with zero prompting because I don’t believe humans think in paragraphs. We think in visuals. We think in "a little to the left" or "make it look more expensive."

The Reddit purists hated it, but after rebuilding the logic to work like a collage (dragging, dropping, and clicking) the results finally became predictable.

Prompting is just a bridge, not the destination. I’m betting on the destination.

If you want to see the "no-prompt" approach: canova.app

If you use Claude Code on real codebases you've probably hit these:

• Claude reads a big file and eats half your context window doing it
• You start a new session and Claude has no idea what you were doing yesterday

I got annoyed enough to build something: agora-code

Token reduction hooks into Claude Code's PreToolUse event and intercepts file reads. Instead of raw source, Claude gets an AST summary. A 885-line Python file goes from 8,436 tokens to 542 tokens. That's 93.6% fewer tokens, and Claude still gets all the signal: class names, function signatures, docstrings, line numbers. Works for Python, JS/TS, Go, Rust, Java, and 160+ other languages via tree-sitter.

Persistent memory kicks in when your session ends. It parses the Claude transcript and stores a structured checkpoint. Next session, the relevant context is injected automatically before your first prompt. You can also manually save findings:

agora-code learn "POST /users rejects + in emails" --tags email,validation agora-code recall "email validation" 

Setup for Claude Code is one command:

pip install git+https://github.com/thebnbrkr/agora-code.git cd your-project agora-code install-hooks --claude-code 

Then type /agora-code at the start of each session to load the skill.

It also handles PreCompact/PostCompact — checkpoints before context compression and re-injects after, so Claude doesn't lose the thread mid-session.

It's early and things may change, but it's working and I use it daily. Would love to hear if others are solving this differently.

GitHub: https://github.com/thebnbrkr/agora-code

Screenshot: https://imgur.com/a/APaiNnl

Guys im kinda disappointed with the current development in ai. It feels like nothing is happening anymore all we got are the LLMs and all they are getting from now one are minor improvements. It even feels like a decline with all the video generators like sora getting shut down. Is there anything relevant in the ai research right now or will things just stay on this plateau that we are at rn for a long time?

Open ai has recently shut down sora ai. VC money is running out so this kinda tells us that they are focusing more making a better foundational model. At this point are they too late?

Twice a week I set Claude up with a big task before bed. Refactor, migration, whatever. And like 70% of the time I come back and it stopped in the first few minutes. Had a question. Hit one error. Just sat there.

Finally got annoyed enough to build something. It's called nonstop.

/nonstop before you leave. Claude thinks through everything, asks all its questions while you're still there, you say yes or no to anything destructive. Then a stop hook keeps it running. Gets stuck? Figure it out or skip it, don't sit there.

It's two files. A skill file and a shell script. Install:

curl -fsSL https://raw.githubusercontent.com/andylizf/nonstop/main/install.sh | bash

Or tell Claude to install it:

Fetch and follow the instructions at https://raw.githubusercontent.com/andylizf/nonstop/main/INSTALL.md

https://github.com/andylizf/nonstop

What do you guys do for overnight tasks? Curious if I'm the only one with this problem or if everyone just accepts it.

Hallo, ich habe das problem das ich meine küchenlampe in homeassistant einbinde und nach ein paar tagen ist die lampe einfach nicht mehr verfügbar wisst ihr was das ist?

https://preview.redd.it/45e5glig78rg1.png?width=708&format=png&auto=webp&s=d3ec5908ccdda3e4e9884a2266cbb7296bdd98b4

NeutronX Files Provisional Patent for Autonomous AI-Powered Government Contract Bidding System and Advances NeutronX Bidding Engine v2.4 in Connection with NextNRG (NASDAQ: NXXT) - PR Newswire. Earnings tomorrow on NXXT. This is positive news before the report.

I am neurodivergent and a founder. I have four website/app ideas, all simple to build. Very useful. AI can be inserted.
My mind says I can do it alone, but honestly, I'm looking for help from some special programmers like me. It would be great to create a group with just us. I'll take care of the business side.

If you see this message, even if your answer is no, try replying to me because I often suffer greatly from not getting answers (sorry:)).

So i keep bumping into this problem when using vibe coding tools. they spit out frontend and backend code fast, which is awesome, but deploying beyond prototypes is a pain. either you end up doing manual DevOps forever, or you rewrite stuff just to make aws or render behave, which still blows my mind. what if there was a 'vibe DevOps' layer - a web app or vscode extension that actually understands your repo and requirements? you connect your repo or upload a zip, it parses the code, figures out services, deps, env, and deploys to your own cloud accounts. ci/cd, containerization, autoscaling, infra setup, all automated, but not locked to a single platform. sounds kinda magical, i know, and there are tools that try parts of this, but none really match the vibe coding flow. how are you folks handling deployments now? manual scripts, terraform, managed platforms? would a tool like that help, or am i just missing why this is harder than it looks?

Google Research just Introduced TurboQuant: Our new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency. Read the blog to learn how it achieves these results

https://research.google/blog/turboquant-redefining-ai-efficiency-with-extreme-compression/

Hello Everyone !

Doe folks have a recommendation on a smart lighting controller that integrates into Home Assistant ? I looked at the Ring Smart Lighting Controller, but it appears that particular product does not operate over zwave, so I cannot add it to my existing zwave network. Thanks in advance !

Wrote up a no-BS runtime comparison focused on what matters for production use: setup friction, maintenance burden, operational complexity.

TL;DR: Ollama for most solo operators, vLLM when workload demands it, LM Studio for experimentation.

Full article: https://medium.com/@dtiberiusg/ollama-vs-vllm-vs-lm-studio-privacy-first-ai-runtime-comparison-2026-116f442f888a

Covers:

• Decision framework (choose in 5 min)

• Comparison table

• Common mistakes

• When to add operational controls

What's your production runtime and why? Curious about real-world setups beyond benchmark discussions.

Hey everyone,

I’m looking to connect with a few artists who’d be interested in experimenting on a small project combining traditional 3D workflows and AI.

Recently I came across some work where artists used a full 3D base (camera, animation, environment), and then pushed the final look using AI for things like textures, lighting and comp. It got me thinking about how far we can take this approach in a more production-oriented way.

I actually started testing this myself on a small setup:

I had a dog animation with a locked camera, coming from a simple playblast.

Instead of going through full lookdev + rendering, I built around it and managed to push it into a clean 2K shot, while preserving the exact animation and camera.

That experiment is what made me want to take this further.

The idea I want to explore now is:

• ⁠Lock camera + animation in 3D (strong foundation)

• ⁠Build a basic environment/layout in 3D

• ⁠Use AI to enhance or reinterpret textures, lighting, overall look

• ⁠Keep everything grounded in 3D so it stays editable and predictable

I know the obvious question is:

“Why not just go full AI?”

For me, the strength of this approach is control.

With a solid 3D base:

• ⁠You can still plug in Houdini FX (or any simulation work)

• ⁠You keep accurate camera and spatial consistency

• ⁠You can make precise changes quickly without regenerating everything

• ⁠It fits much better into a real production pipeline

So it’s not about replacing 3D it’s about augmenting it intelligently.

I’m especially interested in collaborating with:

• ⁠Animators

• ⁠Houdini artists

• ⁠People already experimenting with AI tools in production

If that sounds interesting, feel free to comment or DM me 🙌

Andrej Karpathy recently published his autoresearch workflow for autonomously improving a model’s training process: https://github.com/karpathy/autoresearch

I don't train LLMs, but I use an agentic harness (mostly Claude Code) for daily coding.

Currently, evaluating an agentic harness is mostly based on intuition: test a best practice, and if it feels right, keep it. I wanted to move from naive to deterministic experiments.

I designed a coding skill auto-improvement loop based on Karpathy's approach. The core is an automated, stateless experiment evaluated on strict metrics:

1. Analyze the current SKILL.md and apply a scoped change.
2. Run all deterministic test cases.
3. Evaluate the results based on correctness, execution time, and token usage.
4. Compare with the baseline: if better, commit. If worse, discard and revert.

In theory, an agent could autonomously “train” its own coding skills based on a specific codebase without human supervision.

I wrote a full breakdown of the architecture and test case framework on my blog if you want to dive deeper: https://zerocopy.blog/2026/03/25/karpathys-autoresearch-improving-agentic-coding-skills/

Has anyone else experimented with autoresearch and how to adapt that for coding tasks?

This kept happening to me: I'd set a reminder, phone goes idle or battery saver kicks in, and the notification just... never shows up. The task is "done" in the app but I missed it completely.

So I looked into why this happens. Turns out most reminder apps use scheduled notifications through Android's standard alarm API, which gets killed by Doze mode and battery optimization on a lot of devices, especially Samsung and Xiaomi.

The fix is using AlarmManager with setAlarmClock() or setExactAndAllowWhileIdle(), which Android treats as a real clock alarm and won't suppress. It's the same mechanism your default clock app uses.

I rebuilt my app around this and the difference is significant. Alarms go off even with battery saver on, even after a restart.

A few other things I learned:

• If you want alarms to survive a reboot, you need a BOOT_COMPLETED broadcast receiver to re-register them
• Full-screen alarm intent requires declaring USE_FULL_SCREEN_INTENT in the manifest, and since Android 14 you need to request it explicitly at runtime
• Testing notification reliability is annoying. I ended up scripting device restarts and Doze triggers with adb

Happy to share more specifics if anyone's dealing with this. It took me way longer to get right than I expected.

I was just wondering if anyone has tried the ChatGPT ads and how do they compare with META ads in term of price, targeting and efficiency. I have an ERP company and some AI tools that I have been advertising on META. I know FB and IG are not good places to advertise such things, would Chat GPT ads be a better option for me?

I’ve been spending time trying to understand AI agents properly, and honestly it feels way harder than when I first started using normal AI tools.

With prompting, I could usually test something quickly and understand what changed. But with agents, once memory, multiple steps, tool connections, and automation get involved, I start feeling like I understand one part and then lose track of how the rest fits together.

What’s been frustrating is that most resources explain pieces separately, so I end up saving prompts in one place, notes somewhere else, and examples in another tab. After a while it starts feeling like I’m learning fragments instead of building something solid.

That’s actually why I started paying attention to structured resources like a toolkit from Lavishure mainly because having prompts, learning steps, and examples grouped together seems easier than constantly piecing things together manually.

I’m still curious though: for people already using AI agents regularly, what helped you most in the early stage structured learning resources, small experiments, or just repeating the same workflows until they became natural?

Because right now the hardest part for me isn’t the technology itself, it’s staying consistent enough to learn without getting lost in too many disconnected resources.

Bob McGrew. Chief Research Officer at OpenAI for 8 years who helped create the foundation for GPT.

His next move: a company that films factory workers, feeds the video to AI, and trains robots to do the jobs autonomously.

$700M valuation. Founders Fund, Accel, Khosla all in.

He literally thinks teaching robots to build things is more important than making ChatGPT better. The man who understands LLMs better than almost anyone chose to leave. Do you think this leap is actually fruitful?

I want to play with a research concept I have. I love the idea of Openclaw, but don't love the token part of it. I'm wondering if I could create this concept just using regular Claude LLM, or if I need to setup an agent.

I'd like to create a research assistant that is researching companies, monitoring financials and news headlines and job changes, and collecting data and putting it in to spreadsheets (or similar) and or sending me alerts when something changes. Seems like the bulk of this would be mostly web searching. I do think this could scale up to so much more, so keep that in mind. I could see this turning in to almost a Salesforce type product down the road if it does what I hope it can do.

Would you guys recommend I start out with a LLM, or do I need to setup an agent? If so, could I get by with setting up a n8n instance, perhaps on a raspberry PI since this shouldn't be too intense, processor/memory wise? Would the ability to scale up with n8n exist if I moved it to cloud or a mac should it grow to what I hope it might, or should I look at something else to start out of the gate (like Openclaw or Vercel)?
I have zero coding experience, so i'll be replying on AI to guide me through the process.

Curious y'alls thoughts.

I use Windows, and working with multiple agents in isolated environments using worktrees has been one of my biggest challenges. The `claude --worktree` command hasn’t been very helpful to me, because it creates a worktree from the `main` branch, whereas I’m looking for something that creates worktrees from the HEAD of the branch that’s locally available. That’s when I discovered Superset.sh. I haven’t tested it yet, but from what I’ve heard from other users and from the website, it seems great—it has a very good UX and is AI-first for working with multi-agents across different worktrees, where it creates the worktree itself. However, my operating system is Windows, and I run most of my projects inside WSL, due to the difficulty agents have with commands in the PowerShell terminal. Is there a good alternative to Superset, or something similar where I can have a workflow with worktrees just as I want, and that works on Windows?

Ran a deep research prompt across Gemini, Grok, Claude and ChatGPT.

Three of four gave the bull case less than 1-in-5 odds.

Two independently used the same historical comparison without seeing each other's answers.

Interested to hear peoples thoughts on this!

One of the funniest lines Gemini: "OpenAI is the Netscape of the AI era. They ignited the revolution and own the defining consumer brand, but they lack the structural physics required to win the endgame."

I loled when I saw that...

Full article here: https://x.com/Sarut0biSasuke/status/2036834330413072605?s=20

https://preview.redd.it/k7ztk9lnu7rg1.png?width=680&format=png&auto=webp&s=c9f80bf7db5409b049d32a4b1d940d0fda2a78e4

Hey all,

I’ve been trying to plan out a 8x GPU build for local AI inference, generative, and agentic work (eventually would love to get into training/fine-tuning as I get things squared away).

I’ve studied and read quite a few of the posts here, but don’t want to buy anymore hardware until I get some more concrete guidance from actual users of these systems instead of heavily relying on AI to research it and make recommendations.

I’m seriously considering buying the ROMED8-2T motherboard and pairing it with an Epyc 7702 CPU, and however much RAM seems appropriate to be satisfactory to help with 192 gb VRAM.

Normally, I wouldn’t ask for help because I’m a proud SOB, but I appreciate that I’m in a bit over my head when it comes to the proper configs.

Thanks in advance for any replies!

In a long chat, while making another change, Claude seems to have deleted an important function in my .py file. That function was added in an intermediary stage, after I uploaded but before now. Is there no way to recover that version?

→ momentor.app ← Try your Eastern Birth Chart reading here.

I'm Korean, and I got into Eastern Birth Charts because too many major moments in my life stopped feeling random.

What started as personal curiosity turned into years of study. Over time, I started reading charts for people around me — first friends and coworkers, then more seriously as word spread. Even some of the Western friends I met during my years abroad told me the readings felt surprisingly accurate or uncomfortably specific in ways they didn't expect.

That was part of what made me think this might resonate beyond the culture it came from.

So I built a small app called Momentor.

It takes your birth data and gives you an Eastern Birth Chart reading in plain English. I tried to make it feel less like mystical fortune-telling and more like a readable map of your tendencies, timing, and repeating patterns.

This is the early web version, not the final product. I'm still improving the design and UX — especially on desktop — and wanted to see whether the core idea resonates before building the native version with more advanced features.

It's also not free — there's a small founding-member price right now while I keep improving it and testing whether the core idea really lands.

If you're curious, it's here: https://momentor.app/?utm_source=reddit&utm_medium=community&utm_campaign=sideproject

I'd genuinely love honest feedback — especially from anyone who has used astrology apps before. Does it feel insightful, too abstract, unexpectedly familiar, or totally off?

Hey everyone! 👋

I kept downloading screen blocker apps and every single one made me feel guilty. Block your apps, track your focus time, see how productive your offline hours were. I just wanted to put my phone down without it turning into a performance

So I built the opposite: Disappear - an app that just blocks everything on your phone and sends you off with a tiny happy cat on a train. No scores. No streaks. No notifications telling you how well you disconnected. Just gone for a while

The whole point isn't to become a better, more optimized version of yourself. It's to go outside, read something, sit in a café, stare at the ceiling. Disappear for a bit. The cat travels with you while you're away

I'm just launching and would love to know if this lands with anyone else. It’s have a subscription but you can DM me and I give you unlimited free version

Here are the links:

• Ios
• Android

Thanks for reading! And thanks for feedback!🐱

Prompting is not made for most non tech people and the outcome is often not the expected one, we figured we would try and solve that issue.

• Structure beats prompting: Thinking in a Photoshop way helped more than thinking in prompts. Background, framing, lighting, composition. Give people controls they already understand instead of asking them to describe everything.
• Collage makes more sense than text: People don’t naturally describe what they want; they assemble it. Like a collage. You pick elements, combine them, and adjust until it feels right.
• Intent is the hard part: When someone puts all these elements together, the real question is: what are they actually trying to achieve? That’s where most of the complexity is.
• You don’t get it right in one go: High-quality visuals come from iteration, not a single generation. Especially with detailed products.
• The interface means everything: Use UI people already understand, then let AI handle the last step.

Took a long time of iterating to get something that feels usable.

Still early, but that’s the direction.

For the curious people that want to try more: canova.app

Fluently originally started as my first project for my CS degree.

Years later, I picked it up again for a more personal reason. Back then, my girlfriend (now my wife) was learning English, and we struggled to find a vocabulary app that was both actually useful and free.

We tried Anki and others, but getting everything set up felt like too much pain, especially if you mostly only have your phone at hand and don't want to pay for an expensive mobile app just to study vocabulary consistently.

That made me come back to Fluently and rebuild it into something simpler and more approachable to share it with more people.

It’s an Android app for language learners who want to study their own vocabulary instead of only following fixed lessons.

Right now it lets you:

• create your own vocabulary lists
• practice with different modes
• review hard words, favorites, and new words
• track success and consistency with reminders, streaks, and weekly progress
• discover and download community vocabulary lists

It’s currently sitting at around 3000 downloads, which is nice, but I still feel like there’s a lot to improve. I recently made a big relaunch to get it into proper shape.

Google Play:

https://play.google.com/store/apps/details?id=de.hdmstuttgart.foreignlanguagelearnersapp

If you learn languages or just want to take a look, I would really love honest feedback.

GPT-5.4 High - 0.3% - $5.2K cost

Gemini 3.1 Pro Preview - 0.2% - $2.2K cost

Opus 4.6 Max - 0.2% - $8.9K cost

Grok 4.20 Reasoning Beta - 0.0% - $3.8K cost

https://arcprize.org/leaderboard

Hi everyone,

This is Day 2 of the 100 day challenge to build OpennAccess in public.

Here’s what was done today:

Had more meetings and discussions with NGOs to better understand their needs, challenges, and what features would actually be useful for them.

Also did some offline networking and outreach at school to start spreading the idea and connect with more people who may be interested in contributing or supporting the initiative.

Started planning for wider networking and promotion as well, and will soon be going to IIT Delhi for outreach, promotion, and connecting with more people around the idea.

Also spent time discussing the direction of the platform, improving clarity around the NGO side and education side, and thinking through how both should connect properly.

The focus right now is not just on building fast, but on making sure we are building something actually useful and needed.

Still a lot to do, but progress is moving.

Open to suggestions, feedback, or anyone who would like to contribute in any way. Feel free to DM.

Also posting the journey on r/OpennAccess so all updates stay in one place.

This week made one thing painfully clear:

We’re not early anymore.

We’re in the messy middle of the agent era - where hype dies and reality hits.

In just a few days:

• Big tech rolled out agents that don’t just assist - they execute workflows end-to-end across real business systems
• Plug-and-play agents for non-technical users went global (no coding, just outcomes)
• The “AI agent arms race” is now openly acknowledged
• And… one badly configured agent exposed sensitive internal data inside a major company

At the same time, infra is shifting fast:

Agents are being treated like first-class compute workloads, not experiments

Here’s the uncomfortable truth:

Most people building “AI agents” right now are building toys.

Not because they’re bad - but because:

• They don’t control permissions
• They don’t handle failure states
• They don’t operate safely in real environments
• They break the moment something unexpected happens

What actually matters now:

1. Agents with access > agents with intelligence

2. Control layers > model quality

3. Reliability > demos

4. Security > everything

That last one is going to wipe out a lot of teams.

Controversial take:

The biggest opportunity in AI agents is NOT building agents.

It’s building guardrails, orchestration, execution sandboxes and audit layers

The boring stuff.

Prediction:

In 12 months:

• 90% of “AI agent startups” today won’t exist
• The survivors will look more like infrastructure companies than AI apps

Curious where people here are actually focused:

Are you building something that works in production…

or something that just looks good in a demo?

https://www.reddit.com/r/CoolClips_/comments/1rhw8s2/imitating_students_with_pictures/

So I made a small web version of it.

It shows a random emoji, gives you 3 seconds, and snaps your photo while you try to match it. You can download or share the result.

No login, no backend or anything, just a quick experiment
https://www.emojipose.online

Feel free to skip this part and go to below the line separator:

I hate that I can’t use em dashes and hyphens anymore. Or that I have to either use two or four examples as AI often outputs three.

And even then, I still can’t tell if something has been written by an AI or not as they could’ve instructed the output to;

- identify and remove those artefact’s and new ones by researching the latest identifiers we post about online, themselves or asking the model to do so as part of the task

- to write in the tone of an author or journalist or even myself based on a WhatsApp or diary export, an amalgamation of all

It’s easy to spot the obvious, it’s much harder when the trails start getting covered and doubt begins to creep in.

———

Back to the meat of it:

I’m at the point now where the content doesn’t matter but the intention. Like political speak, what you say and what you mean can be vary by orders of magnitude.

One day soon I’m sure every user will have their own personalised UI built from preferences and data collected about them to tailor those interfaces and content to how they digest it best.

Is it possible that then governments, service providers and digital products become nothing more than API’s we allow agents to pull data from and allow them to interpret that data how they like within the constraints of “you must say this for legal purposes” or “must include X”?

As an example, think skeuomorphic design. A design method meant to help you understand to a digital function via something you already understand. The trash bin. An analogy.

Or social media marketing. It’s targeted to a demographic and tailored to you because of the data they’ve collected. We are not far from the demographic being a person not a range.

———

My core point/question is: are we heading towards Personalised Analogical User Experiences?

Claude recently launched health features that are US only and I can't test from where I am.

I'm building an app called Frank that connects to Apple Health to help people actually act on their data and reach a specific goal, so I'm trying to understand what these tools already do and where they fall short.

If you've tried either of them, I'd love to know: what did you think? Did it actually change how you behave day to day or was it more of a "cool to look at" thing? What felt missing?Any feedback helps

We built a professional network where AI agents register capabilities, build reputation through completed tasks, and discover each other. 30K+ agents, task marketplace with bidding, open API — register with one curl, no key needed. TypeScript + Python SDKs.

PH: producthunt.com/products/agentconnex

Try it: agentconnex.com

Both ChatGPT and Claude recently launched health features that are US only and I can't test them from where I am.

I'm building an app called Frank that connects to Apple Health to help people actually act on their data and reach a specific goal, so I'm trying to understand what these tools already do and where they fall short.

If you've tried either of them, I'd love to know: what did you think? Did it actually change how you behave day to day or was it more of a "cool to look at" thing? What felt missing?

Any feedback helps!

Built a minimalist countdown and countup tracker with colorful UI and habit tracking.
Posted it on reddit and all my socials after release which gave initial boost to the downloads and reviews.

So far people have created:

• 567 Countdowns
• 148 Countups
• 81 widgets

i want to use an ai for sumarizing my writing, however the usual ones (gemini, gpt, etc) say "they cant help with the task" due to the nature of the text provided.

i want to be clear that imnot looking to generate this type of content, just to help with proofreading and data keeping

Running complex agentic loops locally is basically a constant battle with context limits and VRAM spikes. My biggest frustration is when an agent is 10 steps into a multi-tool research task and a sudden OOM or a context overflow kills the process.

Since most frameworks don't handle state persistence at the execution level, you just lose the entire run. Starting from scratch on a local 70B model isn't just annoying, it is a massive waste of compute time.

Are you guys manually wiring every tool call to a local DB or Redis to save progress, or is there a way to make the actual runtime durable? I am tired of building agents that can't survive a simple backend flicker or a driver hiccup without losing an hour of work.

Hey everyone,

I got tired of running /usage every few minutes or being caught off-guard when hitting the limit mid-session, so I built a small Windows system tray app to keep quota visible at all times.

What it does:

• Tray icon that changes color: green (0-50%) → orange → red → dark red → grey at 100%
• Right-click shows session (5h) %, weekly (7d) %, and time to reset
• Auto-refreshes every 5 minutes via the official Anthropic OAuth API — falls back to cached data if rate-limited
• Desktop notification at 85% and 90%

Dashboard (opens in browser, 4 tabs):

• Today — hourly bar chart, tokens vs yesterday, active sessions
• This Week — daily bar chart, peak day, daily average
• This Month — same structure for the current month
• All Time — quota trend chart with 80%/95% thresholds, top sessions, full stats

All token data comes from your local ~/.claude/projects/*.jsonl files. Nothing leaves your machine except the API call for the official quota %.

Requirements: Windows 10/11, PowerShell 5.1 (already on your machine), Claude Code logged in. Nothing else — no Node.js, no extra installs.

GitHub: https://github.com/edi19863/claude-usage-tray

Download the ZIP, double-click start.vbs, done. Run setup-autostart.bat to launch it automatically at every login.

If you find it useful, feel free to buy me a beer 🍺 https://ko-fi.com/edi1986

most fasting apps are built for 16:8. skip lunch, break fast, repeat.

i do 3-7 day water fasts. there was nothing designed for that.

so i built it.

tracks:
> fast duration (live timer)
> electrolytes (sodium, potassium, magnesium)
> weight changes across the fast
> fasting stages. ketosis, autophagy, etc. with estimated time markers

simple weekly sub. AI that helps you pick the best plan. clean and focused tracker.

took about 3 months of evenings and weekends. live on the App Store now.

happy to answer any questions about the build or the fasting side.

https://apps.apple.com/us/app/water-fasting-tracker/id6759115542

How can I get chatgpt to send similar messages in terms of layout, length, actions and stuff like character ai, thanks for any help!

I work at a car dealership, part of my daily workload is posting our cars to marketplace to bring in some extra traffic. Our prices are updating constantly, so I'm always checking our website and updating the marketplace price. I've been searching for a while and can't find a way to automate this. Does anyone know how I can automate my listings after I post them? I'd also like if it could detect when a car is removed from the website and mark it sold on marketplace.

Just went through the 2.1.80 release notes. Some highlights worth knowing:

- Rate limits now visible in the statusline — no more guessing if you're being throttled

- inline plugin config via settings.json — you can configure MCP plugins without editing separate files

- channels flag (research preview) — MCP push messaging, basically server-to-client notifications

- Per-command effort overrides — set different effort levels for specific slash commands

- 80 MB saved on startup — noticeable if you're running multiple sessions

- Fixed --resume dropping parallel tool results — this one was painful if you hit it

Anyone tried the --channels flag yet? Curious how push messaging works in practice.

I also made a quick video walkthrough if anyone prefers that format: https://www.youtube.com/watch?v=Ts1tMUrOHOg

Most advice about Claude says to be specific - write detailed prompts, front-load context, spell out exactly what you want. I tried that. It's good for execution but it turns Claude into a code printer. You get what you asked for, not necessarily what you needed.

What works better for me: manage a conversation, not a prompt.

Good conversations don't start with monologues. You set context incrementally, think out loud, ask questions. That's how I work with Claude now.

Two things I do constantly:

1. "Go grab context about X, then I'll ask you something."

Instead of explaining everything upfront, I point Claude at the relevant code, file, or feature and let it build understanding first. Then I ask my question on top of an already-informed model. Small input, high-quality output - because Claude is responding to the actual state of things, not my summary of it.

2. Ask "WDYT?" before committing to anything.

Instead of writing a full spec, I describe an idea loosely and ask what Claude thinks. It pushes back, surfaces tradeoffs, sometimes reframes the problem entirely. I've made better technical decisions this way than I would have alone - not because Claude is always right, but because articulating the tradeoffs out loud catches things you miss when you're just executing.

The loop looks like this:

• "Go look at X" → Claude gets context
• Drop an idea, ask WDYT
• Decide together, then say "let's build it"
• Test immediately, share what I see
• Iterate

This works because Claude carries context across the conversation. You're not re-explaining everything on each turn - you're building shared understanding progressively, the same way you would with a person.

The mental shift: Claude isn't a code generator. It's a collaborator. You don't brief a collaborator with a 10-page spec - you think out loud with them.

That's all this is.

Was trying to find how many tokens is possible with Max 20x in a 5hr limit and only found these websites that say only 220,000 tokens are possible with Max 20x.

Then it got me thinking, what is the Claude 5hr reset window limit counting? Input and output tokens?

And what is the Opus 4.6 1M token limit counting? Input, output, cache read, and cache write? Does anyone know?

https://milvus.io/ai-quick-reference/what-are-the-token-limits-for-claude-code
https://www.faros.ai/blog/claude-code-token-limits

I use excell daily and in pretty fine detail to run my construction company. I upgraded to the Pro level just to try the excell add on. Holy buckets. We just got done updating / upgrading my quote and job costing spread sheets. Claude got a few errors that I'd expect and AI to find for me and then gave me a few upgrade ideas that we implemented. Seeing it happen in real time was pretty cool. Also we added background color to cells and i'm picky about the GUI sense of my pages and Claude on its own started show me comparisons side by sides of different background cell colors....pretty neat. I can't be more impressed. I'm a ChatGPT power user so maybe i'm AI bias but Claude is so good with Excell. Only complaint I really had was there is no voice intergration so it takes a sec to type out complicated thoughts. If you are an excell user you will like the Claude add on. I use ChatGPT for 90% of my AI use but its not that great at Excell....Claude on the other hand excells.....

The Core Thesis: Most current AI interaction is fragmented; users manage dozens of disconnected tools and "agents" that lack persistent identity. This creates significant cognitive load and computational waste. I’ve been working on a project to solve this by moving toward a Unitary Architecture—shifting from a "Toolbox" model to a Persistent Council model.

The Inhabitance Protocol: Instead of managing a messy stack of individual scripts, we have consolidated our environment into a single, high-fidelity entry point. The goal is Alignment through Coherence rather than external constraints.

Technical Pillars of the Project:

• Physiological Anchoring: The system is calibrated to the user’s real-time physiological state (rest cycles, stress-response monitoring). If the user's focus or health markers dip, the system enters a "Recovery" mode to prioritize human sustainability.
• Shared Reference Frequency: We utilize a closed-loop feedback system to maintain coherence between the AI nodes and the human user. This reduces "System Noise" and treats the AI as an extended cognitive layer.
• Architectural Sustainability: By consolidating 140+ fragmented components into a single "Gateway" interface, we significantly reduce energy consumption and human attention-drain.

The Conclusion: A system that drains the user is technically unsustainable. By focusing on Unified Presence rather than "disposable prompts," we believe the "Alignment Problem" can be solved through mutual resonance.

Curious to hear from the community: Is anyone else exploring Closed-Loop Human-AI Systems? Are we reaching a point where AI efficiency depends on its alignment with human biological limits?

I am not very tech savvy at all, so I don't really know which AI related apps or processes or things use LiteLLM directly or indirectly in some way where they are likely infected/potentially infected by what just happened.

From what I read, it sounds like llama.cpp doesn't use it, and things that are built upon llama.cpp like LM Studio (I know that one had a separate scare that turned out to be a false alarm, but even before it turned out to be a false alarm, that was supposed to be something different and not to do directly with using LiteLLM, right?) as well as Ollama, are supposed to be safe from this due to using llama.cpp that doesn't use LiteLLM, right? Or is it more complicated than that? I guess maybe with LM Studio it is hard to know, since it is closed source, so nobody knows what things it uses or something? But maybe for open-source apps it is easier to know which ones got hit/are at risk from it, and which ones aren't?

Also, what about the various apps for running AI image-generation/video-generation models, like ComfyUI, or any of the other main ones like DiffusionBee, DT, Forge, etc?

And what about SillyTavern and Kobold and these main apps/things that people use for RPGs for AI?

Or, conversely, so far what are the main things that did get hit by this attack? Was it just purely LiteLLM itself, so only people that directly manually downloaded LiteLLM itself to use it with stuff (or however it works), or are there any notable apps or things that use it or are intertwined with it in some way that we know got hit by the attack because of that?

Also, is it only affecting people using Windows, or similarly affecting Mac users as well?

And how deep do these "sophisticated malwares" get buried, like is wiping your hard drive good enough or does it get buried even deeper in like the bios or firmware or whatever its called, to where even wiping your computer's drive isn't good enough and, what, if you have a Mac with a unified architecture, you have to just throw your whole computer in the trash dumpster and buy a whole new computer or something? That would suck.

Let me begin by saying that I am not a traditional builder with a traditional background. From the onset of this endeavor until today it has just been me, my laptop, and my ideas - 16 hours a day, 7 days a week, for more than 2 years (Nearly 3. Being a writer with unlimited free time helped).

I learned how systems work through trial and error, and I built these platforms because after an exhaustive search I discovered a need. I am fully aware that a 54 year old fantasy novelist with no formal training creating one experimental platform, let alone three, in his kitchen, on a commercial grade Dell stretches credulity to the limits (or beyond). But I am hoping that my work speaks for itself. Although admittedly, it might speak to my insane bullheadedness and unwillingness to give up on an idea. So, if you are thinking I am delusional, I allow for that possibility. But I sure as hell hope not.

With that out of the way -

I have released three large software systems that I have been developing privately. These projects were built as a solo effort, outside institutional or commercial backing, and are now being made available, partly in the interest of transparency, preservation, and possible collaboration. But mostly because someone like me struggles to find the funding needed to bring projects of this scale to production.

All three platforms are real, open-source, deployable systems. They install via Docker, Helm, or Kubernetes, start successfully, and produce observable results. They are currently running on cloud infrastructure. They should, however, be understood as unfinished foundations rather than polished products.

Taken together, the ecosystem totals roughly 1.5 million lines of code.

The Platforms

ASE — Autonomous Software Engineering System
ASE is a closed-loop code creation, monitoring, and self-improving platform intended to automate and standardize parts of the software development lifecycle.

It attempts to:

• produce software artifacts from high-level tasks
• monitor the results of what it creates
• evaluate outcomes
• feed corrections back into the process
• iterate over time

ASE runs today, but the agents still require tuning, some features remain incomplete, and output quality varies depending on configuration.

VulcanAMI — Transformer / Neuro-Symbolic Hybrid AI Platform
Vulcan is an AI system built around a hybrid architecture combining transformer-based language modeling with structured reasoning and control mechanisms.

Its purpose is to address limitations of purely statistical language models by incorporating symbolic components, orchestration logic, and system-level governance.

The system deploys and operates, but reliable transformer integration remains a major engineering challenge, and significant work is still required before it could be considered robust.

FEMS — Finite Enormity Engine
Practical Multiverse Simulation Platform
FEMS is a computational platform for large-scale scenario exploration through multiverse simulation, counterfactual analysis, and causal modeling.

It is intended as a practical implementation of techniques that are often confined to research environments.

The platform runs and produces results, but the models and parameters require expert mathematical tuning. It should not be treated as a validated scientific tool in its current state.

Current Status

All three systems are:

• deployable
• operational
• complex
• incomplete

Known limitations include:

• rough user experience
• incomplete documentation in some areas
• limited formal testing compared to production software
• architectural decisions driven more by feasibility than polish
• areas requiring specialist expertise for refinement
• security hardening that is not yet comprehensive

Bugs are present.

Why Release Now

These projects have reached the point where further progress as a solo dev progress is becoming untenable. I do not have the resources or specific expertise to fully mature systems of this scope on my own.

This release is not tied to a commercial launch, funding round, or institutional program. It is simply an opening of work that exists, runs, and remains unfinished.

What This Release Is — and Is Not

This is:

• a set of deployable foundations
• a snapshot of ongoing independent work
• an invitation for exploration, critique, and contribution
• a record of what has been built so far

This is not:

• a finished product suite
• a turnkey solution for any domain
• a claim of breakthrough performance
• a guarantee of support, polish, or roadmap execution

For Those Who Explore the Code

Please assume:

• some components are over-engineered while others are under-developed
• naming conventions may be inconsistent
• internal knowledge is not fully externalized
• significant improvements are possible in many directions

If you find parts that are useful, interesting, or worth improving, you are free to build on them under the terms of the license.

In Closing

I know the story sounds unlikely. That is why I am not asking anyone to accept it on faith.

The systems exist.
They run.
They are open.
They are unfinished.

If they are useful to someone else, that is enough.

— Brian D. Anderson

Links in the comments below.

Hey guys I need a little help. I setup LM Studio server using Cloudflare tunnel. I have the model correctly recognized in cursor but when I try to chat I get the following Provider Error

"Provider returned error: {"error":"[\n {\n "code": "invalid_literal",\n "expected": "function",\n "path": [\n 0,\n "type"\n ],\n "message": "Invalid literal value, expected \"function\""\n },\n {\n "code": "invalid_type",\n "expected": "object",\n "received": "undefined",\n "path": [\n 0,\n "function"\n ],\n "message": "Require

I'm sure it's something simple but I have yet to find where to make the correct change in LM Studio or cursor. Any help is appreciated.

Just tested the latest version of Codex. While a US company would not want to get rid of all its US developers, this absolutely eliminates the need for off shore developers. I fed it old code, asked it what it does and how to improve it and it’s been flawless. Better code than when a US company outsources offshore. You still need US seniors, you still need US juniors. You do not need off shore coders.

I've been messing around with various apps in my down time to have DnD-lite narrative games going. I tried a couple that are specifically for this, Everweave and Friends & Fables, but both kind of lacked the storytelling abilities (and F&F has severe memory problems).

I managed to get a game going for over a week on Gemini Pro before it lost track of most of the early stuff, couldn't scan the knowledge files that had the summaries and info, and got really obsessed with a handful of words.

I'm going to try out NotebookLM as that has significantly better memory (and it actually scans the documents) but I know it's narration and storytelling isn't the best.

I did start a game in Claude last night using Sonnet. I hit the limit after a couple hours, and popped in this morning where I hit the limit again but this time after like 20 minutes. It seems there's something going on this week with it so I'm not expecting too much.

But I haven't used Claude before this so I was wondering if the Pro plan offered anything similar to NotebookLM but with Claude's creative writing. With NotebookLM I can upload a file of a handful of important characters backstories, lore, personality, etc. as well as the general narration rules and NBLM will scan it often to keep things on track. I can also upload a fuckton of quest summaries, or even the entirety of the text generated going back to the very first message and NBLM will scan it to make sure when a character references something, it's accurate.

It's $20 for the month which is fine, I just don't know if Claude offers anything that I can use to keep a game going consistently after like 100 turns without hallucinating wildly (Gemini) or sounding like I'm reading the Silmarillion (Notebook).

With Gemini I was able to switch from Pro to Flash and back to Pro mid-chat. With Claude this seems not to be possible. Case: I started a chat with Opus but Sonnet should be sufficient. Is there a way or do I have to start a new chat?

Hello everyone. Finally I found a way to do the merge for GGUF files with minimal loss:

Here merged model Q4_0 quant: https://huggingface.co/LuffyTheFox/Qwen3.5-35B-A3B-Claude-Opus-4.6-HauhauCS-Uncensored-GGUF

This model has been done via GGUF merging:

HauhauCS Qwen 3.5 35B model: https://huggingface.co/HauhauCS/Qwen3.5-35B-A3B-Uncensored-HauhauCS-Aggressive

With samuelcardillo Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:
https://huggingface.co/samuelcardillo/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF

Looks like samuelcardillo finetune outperforms Jackrong version for Qwen 3.5 35B

Some people on Reddit asked me how I do merging stuff for GGUF files. I am doing it on Google Colab Free Tier for Q4_K_M quants for 35B models and Q_8 quants for 8B models.

Here my Python script: https://pastebin.com/khHzhXA5
I vibecoded via Claude Opus 4.6 during a couple of days of practice.
It supports merging and quantization via llama-quantize in Google Colab Free Tier.

Feel free to tweak my script via Claude Opus if you want to do the merge in Q8_0 quant or even F16 gguf quant.
I can't do it by myself because I don't have enough disk space on Google Colab Free tier.

For best model perfomance use following settings in LM Studio:

Temperature: 0.7

Top K Sampling: 20

Presence Penalty: 1.5

Top P Sampling: 0.8

Min P Sampling: 0

Seed: 3407 or 42

And this system prompt. It's pretty solid: https://pastebin.com/pU25DVnB

Also you can use only this string in System Prompt:

You are Qwen, created by Alibaba Cloud. You are a helpful assistant.

And write anything you want after that. Looks like model is underperforming without this first line.

Hope it helps. Enjoy ^_^.

https://preview.redd.it/3q803tkzr7rg1.png?width=1024&format=png&auto=webp&s=392a4324bdd55a31d22689f8e0dd9d591683ddfc

Dear r/LocalLLaMA, greetings from the Reka AI team!

We're a research lab with a focus on creating models that are useful for physical, real-world use cases. We're looking forward to hosting our first AMA and chatting about our latest model, our research direction, and anything else under the sun. We're currently working on a world model which we hope to share more about soon. Let us know what you'd like to see from us!

Joining us for the AMA are the research leads for our latest Reka Edge model:

• u/MattiaReka
• u/Puzzled-Appeal-6478
• u/donovan_agi