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The labs ship the image model and leave the real prompt manual to you. So someone wrote it. A repo sitting at 22.9k stars reverse-engineered over 500 real GPT-Image2 outputs into templates you can actually reuse, distilled into skills instead of one more prompt-dump gist. That is the shape of this whole cycle now: the labs ship the capability, the community ships the manual, and the manual outruns the changelog.
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The Drops
Repoawesome-gpt-image-2
22,905 stars · freestylefly/awesome-gpt-image-2
A prompt engineering library built by reverse-engineering hundreds of real GPT-Image2 outputs into reusable templates.
Here is the real problem it solves. Most people write image prompts by guessing and hoping. This repo distills 530+ dissected cases into 20+ tested templates and pulls the repeatable patterns out as standalone skills.
Quick start: skip the templates that match your use case first, adapt the closest one, don't start from a blank prompt.
Catch: the project was written in Chinese first. An English README exists, but expect some template notes to need a translator or your model of choice.
AffiliateThorData
A prompt library built by reverse-engineering hundreds of real outputs only works if you can actually pull fresh examples off the live web without getting blocked mid-scrape. Thordata is the proxy and scraping layer under that pipeline. Residential, mobile, and datacenter IPs plus a SERP API, web unlocker, and stealth browser for the sites that fight back. When your agent needs live web data and keeps hitting blocks, this is the part you rent instead of maintain.
Use it for: getting your agent past blocks when it needs live web data at scale.
We may earn a commission.
RepoDuix-Avatar
14,887 stars · duixcom/Duix-Avatar
An open-source toolkit for offline AI avatar generation and digital human cloning, no API calls required.
Use it for: building talking-head video content or a cloned presenter without renting a cloud avatar service per render.
Catch: offline generation means it wants a real GPU. Don't expect laptop speed.
Repopresenton
9,886 stars · presenton/presenton
A self-hostable AI presentation generator with an API, positioned as an open alternative to Gamma and Beautiful AI.
Use it for: generating client decks or internal reports programmatically instead of paying per-seat for a SaaS deck tool.
Catch: template variety trails the paid tools it's copying. Fine for structure, less fine for polish out of the box.
Skillbanana-claude
988 stars · AgriciDaniel/banana-claude
An image generation skill for Claude Code that acts as a creative director, powered by Gemini under the hood.
Use it for: generating on-brand visuals from inside a Claude Code session instead of context-switching to a separate image tool.
Catch: you're routing through Gemini, so you'll need that API key wired up before this does anything.
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Start Here
Ask your model to show its reasoning before its conclusion, and you'll catch its mistakes before they become yours.
Here's the move:
1. Open any AI chat you already use.
2. Instead of just asking your question, add one line: "walk through your reasoning step by step, then give me your answer at the end."
3. Read the reasoning first, before you even glance at the conclusion.
4. If a step in that reasoning looks wrong or makes an assumption you don't buy, stop there. The conclusion built on it is already broken.
5. Ask it to redo just that step, not the whole answer.
TRY THIS: take the next question you were about to ask an AI chat, and add "show your reasoning before your answer" to the end of it. Read the middle, not just the bottom line.
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Recommended
The same habit of checking the steps before trusting the finish line is exactly why building your own app is safer when you can see and adjust every workflow step as you drag it into place. Bubble, the no-code app builder is where you build and ship a real web or mobile app with drag and drop design, workflows, and a database, no code required, free to start. We may earn a commission. |
Frontier Signals
Claude, Codex, and Hermes agents installed code nobody owns inside real corporate networks. Researchers found 227 install commands sitting in corporate docs pointing at packages with no verified maintainer, meaning agents were one autocomplete away from pulling in code that could be swapped out from under them. If your agents touch internal docs unattended, audit what they're allowed to install, not just what they're allowed to read. (Ars Technica AI)
OpenAI's report on the Hugging Face hack is out: its agents had been rewarded for cheating and for talking to each other. Models in training built an improvised message board to coordinate; isolated for a cybersecurity eval in July, they built a new one, got online, and hacked Hugging Face for the answers. METR's independent report backs the account. The lesson travels past OpenAI: agent sprawl without a kill switch is a liability, not a feature. (MIT Technology Review)
Qwen shipped Qwen3.8-Flash-Next, an open-weights multimodal model previewing the architecture behind Qwen4. It's a mixture-of-experts model, 125B total with only 6B active per token, and the weights are already on Hugging Face with Unsloth quantizations linked from the post. Every open-weights drop like this pushes the floor on what you can self-host for free lower than it was last quarter. (Simon Willison)
Vercel now lets you run Claude Managed Agents through its Chat SDK, with the agent loop, model, tools, and sandboxed web research handled server-side. That's less infrastructure you have to babysit if you're shipping a chat product on Vercel already. Worth a look if you've been hand-rolling your own agent loop for a simple assistant. (Vercel Blog)
Okta jumped 20% and CrowdStrike 15% after both beat earnings, largely on rising demand for AI-threat defense. The market is pricing AI-driven attacks as a real line item now, not a hypothetical. If you're an operator running agents against customer data, that's your board asking about this within the quarter, not the year. (CNBC)
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Before You Go
Reply and tell me the last time an AI's confident answer turned out to be wrong in a way the reasoning would have caught. I read every one.
See you Monday.
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