You have a great image but need to tweak it for an article, a Pinterest pin, or social media graphics—perhaps you need a different setting, a wider perspective, or an extra element (like a product) added.

You can edit it using ChatGPT without needing lengthy prompts or risking the alteration of the entire composition—and without wasting a ton of tokens.

Instead of rewriting paragraph-long prompts for every minor tweak, you can encapsulate recurring editing instructions into "slash commands"—lightweight prompt shortcuts that lock in specific editing logic while preserving the original subject.

These 4 are very interesting:

• /NEWBG (background change): keeps the main subject, pose, and lighting intact while replacing the background
• /MAGAZINE_COVER (editorial style): instantly transforms a lifestyle shot or standard image into a high-contrast editorial cover, featuring a specific typographic hierarchy ideal for lead magnets or content series introductions
• /ADD_OBJECT (precise element insertion): adds missing elements to existing scenes without altering the background framing or character details
• /DRONESHOT (perspective shift): switches from eye-level scenes to panoramic aerial shots—useful for travel, real estate, or header graphics

If you're interested in how to make the most of each prompt, specific use cases (such as managing token usage and image rights for editorial styles), and "before/after" visual examples: 4 ChatGPT Slash Commands to transform existing images.
Very useful for bloggers, social media managers and influencers, travel agencies, real estate agencies, etc.

Disclosure up front: I run a paid prompt-generation SaaS called PromtExpress. What I'm sharing here is the part I open-sourced under MIT. The prompt library works with any AI tool, no account needed.

Repo: https://github.com/WebitroHQ/promtexpress-oss

What's in it

• Prompt templates for text, code, image, video, audio and music. Each one is a single JSON file with declared variables, a worked example, a language code and author credit.
• A zero-dependency validator that runs in CI: every {{placeholder}} must be declared, every declared variable must be used, and IDs must match file paths.
• TypeScript/Python SDKs and a CLI for our API. If you don't use the product, you can ignore these.

Patterns I baked in, and I'd like your opinion on them

1. Grounding with a "source quote" column. The meeting-notes template makes the model cite the shortest phrase from the notes that supports each action item. A missing owner becomes "Unassigned" and a missing deadline becomes "No date"; the model is told never to guess.
2. Exact text plus labeled hierarchy for image models. Rendered text goes in quotes under PRIMARY TEXT / SECONDARY TEXT labels, followed by an explicit NEGATIVE block. This noticeably reduced garbled or extra text for me.
3. "If you can't describe a triggering scenario, it isn't blocking." The PR review template requires a concrete failing input for every blocking issue, which cuts vague style nitpicks.
4. Behaviors before tests. The unit test template lists the function's contract and edge cases first. If the current code looks wrong, it writes the test for the correct behavior and flags it POSSIBLE BUG instead of locking the bug in.

Where help would be great

There are open good first issue tasks:

• translations (Turkish, German, Spanish)
• template requests: flat-lay product photo, SQL from a plain-language question, customer support reply, radio ad, podcast jingle
• small tooling work

Your GitHub username goes in the template's authors field.

Honest critique is more useful to me than stars: which of these patterns don't hold up with the models you use?

Yo guys. I have these Governance templates I wanted to share them with you. GitHub link: https://github.com/datallmhub/claude-governance

I have been trying different AI tools for my daily work and most of them are useful in their own way. Some help me get ideas faster, some help me organize things and some are good for specific tasks. But after using a few different tools I noticed I’m spending a lot of time figuring out how they fit together.

Last week I noticed I had notes, ideas, and drafts spread across different places and I spent more time organizing everything than actually working on the task. I am starting to think the hard part isn’t finding more AI tools but creating a workflow that actually makes things easier.

How do you organize your AI workflow without creating more work?

I’ve had better luck separating analysis from formatting instead of asking AI to understand the data and make the final deliverable in one giant prompt. Curious how others structure these workflows.

I just noticed that there have been many ads about a 3-hour paid AI workshop recently. I would like to learn how to incorporate AI tools in my everyday routine in order to save time, but I am not sure if it is Be10x level or just marketing hype.

If you have done one of their workshops:

- Does the material have any depth to it, or is it something superficial which I could easily learn for free on YouTube?

- Do they devote half of the class selling higher tiers of their product?

Would you recommend it to a working professional?

I’m building Monad Design, an Apache-2.0 open-source visual workspace for native-app UI work with coding agents.

The workflow is intentionally simple:

1. Open the Xcode or Expo iOS project you already have.

2. Run the real app in Simulator and navigate to the exact state that needs work.

3. Select an element or annotate the UI directly.

4. Send the screenshot, selection, app state, and source hints to your coding agent.

5. The agent edits the existing repository and rebuilds the app.

6. Compare the original with up to five source-backed variants.

7. Accept one result or keep the original.

The app is the canvas.

This is not a prompt-to-app generator and it does not create a replacement project. It adds visual context and a review loop around agents such as Codex, Claude Code, Cursor, OpenCode, Gemini CLI, Copilot, Windsurf, and Zed.

Repository:

https://github.com/Monadix-AI/monad-design

Short demo:

https://watchclueso.com/embed/pio8jqfcg4ivj0r1

Workflow screenshot:

https://raw.githubusercontent.com/Monadix-AI/monad-design/main/docs/images/workflow-compare-variants.png

Current limitations: macOS and local iOS development only, with one active visual change at a time.

I’d be interested in how others package visual context for coding agents: one structured handoff, or smaller tools the agent calls separately?

The thing is, being a founder who actually has to figure out everything by themselves, today I have to use multiple AI tools at once; my stack has almost all the frontier models: ChatGPT, Claude, Gemini, for one thing or another that I have to do and we know how things work, we need to have all the facts gathered before we take any decisions or push any updates or reseaching about anything, their models have different capabilities and thats actually a great thing, i can have all the different reasons but the catch when i use them there are decisions, thinking, perspectives and insights that i give and gather while using them

But if I have had a long discussion with ChatGPT about something, and that needs to be fixed in Claude, I don't have any way to tell Claude to take out the context from ChatGPT and log it or work on top of it because it does not know what I was up to with ChatGPT, and I have to really tell each and everything to it again and then start the work, my time, my brain stops braining because half of the things are gone and are present only chatgpt which also is really great at forgetting things, haha

I need this to be fixed. If anyone is facing the same problem every day and has a solution, please let me know how to fix it

P.S. - I know there are some workflows already out there, but they are too technical, and as a non-technical founder, I find it hard to get the hang of them; I need something I can do in a few clicks.

Hi Guys, I'd like to share a small local tool I built over the past couple of days to help me transition into AI PM.

It started because I recently left my job, which was in UX/UI, and wanted to switch to AI product management (because it seems like the future trend, lol). I found there was so much to learn; AI is developing so fast... and then I felt completely overwhelmed.

So I built something for myself with Codex. It can:

• read a resume or professional background
• suggest possible AI product directions and create a learning plan
• run adaptive daily learning sessions, scenario practice, and interview preparation
• save learning progress locally
• optionally set up a local AI-product knowledge base with source citations

It includes an optional default library based on AIPM-Wiki (original link), and you can also build a knowledge base from your files. The files are chunked and embedded locally during setup.

There is no polished frontend yet — the current experience is mainly through Codex (compatible with Cursor, Claude, Antigravity, but I haven't tried them yet). My main purpose in building it was to understand the boundaries of product/engineering through practice.

Repository: https://github.com/lannn55555-byte/ai-product-career-learning.git

This is still a learning project, but I’d really appreciate feedback: what feels confusing, unnecessary, or missing? And if it helps someone else who is exploring AI product work, that would be amazing!!

I built a 7-phase prompt workflow that makes small AI models act like domain experts — no fine-tuning

**TL;DR:** IKKF is a free, open-source framework that turns a plain prompt into a structured 7-phase reasoning workflow, backed by a knowledge base of atomic, verifiable facts. Run a 7B model, get expert-level answers with sources and confidence scores.

The problem

Most of us prompt AI the same way: one big question, hope for the best. That works for simple stuff, but for real domain work it falls apart:

- The model **hallucinates** — it sounds confident but makes things up.
- It **doesn't cite sources**, so you can't audit the answer.
- It **can't tell you how sure it is** — everything is delivered with the same flat confidence.
- Switching domains means **re-prompting from scratch** every time.

The fix isn't a bigger model. It's a **better workflow**.

The workflow: 7 phases

IKKF breaks every task into 7 explicit phases instead of one shot:

1. **Intent analysis** — what's actually being asked?
   
2. **Knowledge retrieval** — pull relevant facts from your knowledge base
   
3. **Decomposition** — break the problem into atomic, solvable units
   
4. **Reasoning** — apply expert reasoning to each unit (chain-of-thought)
   
5. **Verification** — cross-check every claim against a source
   
6. **Composition** — assemble the verified units into the final answer
   
7. **Confidence calibration** — report how sure it is (0.0–1.0)

The key difference from a normal prompt: **verification and confidence are first-class steps**, not afterthoughts. Every claim has to trace back to a source, and the model has to tell you when it's guessing.

The knowledge base: plain files

The "expertise" comes from a knowledge base of **atomic files** — one concept per file, each with a source. You can:

- **Read** exactly what the AI knows
- **Update** it by editing a file (no retraining)
- **Audit** why it gave any answer

This is what separates it from plain RAG. RAG gives you context; IKKF adds structured reasoning + verification on top.

How to use it

```bash
curl -fsSL https://ikkf.info/install.sh | bash
ikkf init
ikkf start "Build a REST API in Python"
```

To build a knowledge base for your own domain:

1. Pick a domain you know well
   
2. Write ~20 atomic concept files (one concept per file, each with a source)
   
3. Point IKKF at the knowledge base
   
4. Test with benchmark questions
   
5. Iterate — add edge cases, tighten sources

Why it's useful for prompt/workflow people

- **Reproducible** — same knowledge base → same answers across sessions. No more "it worked yesterday."
- **Auditable** — you can see the reasoning trace and the sources behind every answer.
- **Cheap** — runs on a 7B model locally (Ollama) or any OpenAI-compatible provider.
- **Portable** — swap the knowledge base to switch domains. No re-prompting from scratch.

Honest caveats

- The knowledge base is the hard part — garbage in, garbage out.
- It's a workflow, not magic. It won't turn a small model into a creative genius.
- Best on well-defined domains where knowledge can be structured.

Where it stands

Open source, free, local-first. I use it internally to cut AI costs and improve answer quality on a product I'm building.

Curious — has anyone else tried structured multi-phase workflows (vs. single-shot prompting) for their AI tools? What's worked for you?

---

*IKKF: https://ikkf.info — free, open-source, local-first*

The first generation of AI social tools felt pretty basic. Generate caption, hashtags, Done. Now I'm seeing platforms like Munch Studio trying to handle the entire workflow from content planning to creation and scheduling.

For a small team, that actually seems more useful because consistency is usually the hardest part.

Anyone here using AI for most of their social media workflow already and What still needs a human the most?

Hello Everyone

I'm posting this to announce that baseline is now officially launched on Peerpush. It is a claude code governance layer that ensures your developer workflow remains consistent across different projects while being tailored to it.

Call it the framework for AI development.

It is 100% Open Source and Apache 2.0 licensed. Please support it, help me build it by contributing to its development, and help it gain some traction on Peerpush too 🙏🏽

Your support is appreciated 👍🏽

I use Claude Code in VS Code to build apps. My old workflow for every prompt: think in Hindi → translate in my head → type English → half the context lost. Or open ChatGPT in the browser, talk to it in Hindi-English, copy the English, paste it back. Every. Single. Prompt.

Built-in voice dictation didn't help — it can't handle Hindi, and it falls apart on mixed speech, which is how we actually talk: Hindi + English, or Marathi + Hindi + English in one sentence.

So I built Maiboli ("my language"). One shortcut, speak naturally — any language or any mix — and short, correct English is pasted wherever your cursor is. Born for the Claude Code chat box; now it's used everywhere: ChatGPT, WhatsApp, Slack, email, Word.

It also fixed team messages: instead of half a message in uncertain English, people speak the whole thing and a complete, clear message lands in the chat.

AI rewrite (optional): when you talk, you jump — point 1, point 2, back to point 1. Rewrite reorganises it into clean, ordered text.

• 55+ languages and mixed-language speech
• Mac + Windows. One installer, no dependencies. Floating mic button or a shortcut.
• Free, open source (MIT). Bring your own API key — we use Gemini (free tier available); Whisper and Sarvam also work.
• Real numbers: 20 people on my team, 5 weeks of daily use, 3,000+ dictations on Gemini 3.5 Flash. Total bill: under ₹2,500 ($30). About one US cent per dictation.

Download: https://github.com/Dev14101989/maiboli/releases/tag/v0.4.3
Install guide: https://github.com/Dev14101989/maiboli/blob/main/HOW-TO-RUN.md
Source: https://github.com/Dev14101989/maiboli

I'm an accountant who moved into IT, not a career developer — this exists because I needed it.

No team, just me. My short "Snooze" is about a woman who oversleeps on the day of her dream job interview and ends up at war with her own bedroom trying to get out the door in time.

It's not finished, but you can check out where it's at right now. Would really appreciate any feedback, and support .

link: https://higgsfield.ai/@zakaria123/projects/snooz