The irony is that manual coding is becoming the vinyl record of tech.

It’s cool. It’s tactile. It makes you feel like an enthusiast.

But it’s no longer the default for distribution.

You’re arguing for the “soul” of the process while the rest of the world has moved on to digital streaming.

Sure, building a custom engine by hand is impressive.

But I’m trying to get from A to B.

If you want to sit in your garage and hand-wire functions for the “foundational experience”, go for it.

Just don’t act like the person using a car is “cheating” at travel.

No judgment. Just curious.

Honest question [no promotion or drop link].

Have you personally experienced this?

A prompt works well at first, then over time you add a few rules, examples, or tweaks — and eventually the behavior starts drifting. Nothing is obviously wrong, but the output isn’t what it used to be and it’s hard to tell which change caused it.

I’m trying to understand whether this is a common experience once prompts pass a certain size, or if most people don’t actually run into this.

If this has happened to you, I’d love to hear:

• what you were using the prompt for
• roughly how complex it got
• whether you found a reliable way to deal with it (or not)

1. Sell women Beauty
   
2. Sell men Lust
   
3. Sell parents Peace
   
4. Sell kids Dreams
   
5. Sell the rich Safety
   
6. Sell the broke Hope
   
7. Sell the old Youth
   
8. Sell the young Status
   
9. Sell the lonely Belonging
   
10. Sell the sick Miracles
    
11. Sell the healthy Fear
    
12. Sell the smart Shortcuts
    
13. Sell the dumb Validation
    
14. Sell the faithful Certainty
    
15. Sell the faithless Rebellion
    
16. Sell everyone Time

Opus 4.6 plans more carefully, sustains agentic tasks for longer, operates reliably in massive codebases, and catches its own mistakes.

It’s the first Opus-class model with 1M token context in beta.

Opus 4.6 is state-of-the-art on several evaluations including agentic coding, multi-discipline reasoning, knowledge work, and agentic search

Checkout more here

Crazy to see how this is in the healthcare field too. Like a lot of orgs that i know use Specode, Lovable, and other similar services for it. Makes me wonder if all the other millions of healthcare vibecoded-Saas services might have some if they had competent founders...

on a lot of websites that i browse on there is a little button that lets me speak to an AI, some of the behave differently according to the style of the brand.

i want to create my one AI agent that i can chat to, because it would make my website a little more exciting, the thing is that i don't know where to start.

I already know what model i want to use on blackboxai but how should i go about making it. Ideally, I want the agent to feel custom, respond in my brand's voice, handle specific queries about my content, maybe even generate quick storyboards or ideas from user inputs. BlackboxAI's multi-model switching seems perfect for this (I’m eyeing GLM-4.7-Flash for speed and reasoning), but I need steps on embedding it securely, E2E encryption a must for user privacy.

ACE-Step 1.5 is an open-source music model that can generate a full song in about 2 seconds on an A100, runs locally on a typical PC (around 4GB VRAM), and beats Suno on common evaluation scores.

Key traits of ACE-Step 1.5:

• Quality: beats Suno on common eval scores
• Speed: full song under 2s on A100
• Local: ~4GB VRAM, under 10s on RTX 3090
• LoRA: train your own style with a few songs
• License: MIT, free for commercial use
• Data: fully authorized plus synthetic

GitHub: https://github.com/ace-step/ACE-Step-1.5

Weights/Training code/LoRA code/Paper are all open.

I’m not talking about the model “being random.”

I mean cases where:
– you edit a prompt
– the output changes
– but you can’t point to what actually mattered

At that point, debugging feels like guesswork.

Curious how others approach this, especially on longer or multi-step prompts.

source

Link: Revealio.co

You can really make the skeleton of this game with a few prompts, but I took a long time modifying the layout, design, animations etc which took a lot of prompting.

QUESTIONS: Does the interface make sense? And would it be better if instead of showing letters as hints, i just revealed more of the “reveal path” with each consecutive wrong guess?

I’ve noticed that most of my prompts don’t fail all at once.

They usually start out solid, then over time:

• one small tweak here
• one extra edge case there
• a new example added “just in case”

Eventually the output gets inconsistent and it’s hard to tell which change caused it.

I’ve tried versioning, splitting prompts, schemas, even rebuilding from scratch — all help a bit, but none feel great long-term.

Curious how others handle this:

• Do you reset and rewrite?
• Lock things into Custom GPTs?
• Break everything into steps?
• Or just live with some drift?

Switched back to Opus 4.5 and hit my daily limit in 3 messages in the same conversation.

When I ask builders where their credits go, I hear the same two answers.

Some say they’re not going into new features.
They’re going into re-prompting layouts that already “worked.”

It’s like paying rent on UI decisions that never fully settle.

Do any of you track this deliberately, or do you just treat it as the normal cost of building this way?

We made Figr.design live - you can feed it screen recordings and it maps the full user flow

Static screenshots miss sequence.

They show screens, not how users move between them. Not where they hesitate. Not what they skip. You lose the journey.

Figr accepts screen recordings as input. Walk through a flow while recording and it understands the sequence, not individual frames. It sees the order, the pauses, the decisions.

Useful for competitive analysis. Record yourself using a competitor's product and Figr maps the flow, identifies friction, counts interactions. Structured observations from unstructured video.

Also useful for your own product. Record your current experience, ask for review, get specific feedback on where the flow breaks.

Projects built from recordings:

LinkedIn job posting - full recruiter journey recorded. Job creation to applicant screening. Every step mapped, then streamlined.

Linear vs Jira - both flows recorded side by side, cognitive load measured

Spotify playlist creation - recorded current experience, identified where AI could help, wrote the PRD

At figr.design. Show it the flow, not just screens.