"When AI agent (Claude Code, Codex, Cursor, OpenCode, or compatible client) encounters an APK, a binary, frontend JS encryption, a CTF challenge, or a pentesting target, this package routes it to right methodology, checks available tools, executes a repeatable workflow instead of guessing commands."

https://github.com/zhaoxuya520/reverse-skill

Community Overview: https://lifehubber.com/ai/resources/reverse-skill/

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

New resources are added regularly — feel free to join the sub for updates.

Full searchable archive of all resources posted so far on our community site, LifeHubber: https://lifehubber.com/ai/resources/ 200+ open-ish AI models, agents, tools, datasets, and related resources, with filtering and sorting.

u/Kissthislilstar — 8 days ago

▲ 1 r/PromptEnginering

Uncensored

reddit.com

u/Kissthislilstar — 12 days ago

▲ 116 r/PromptEnginering+2 crossposts

Antigravity CLI Commands Cheat Sheet

>This is taken from the following tweet: https://x.com/googleaidevs/status/2085468449145544780

We’ve put together this cheat sheet of essential slash commands to help you navigate, customize, and execute tasks faster than ever in Google @Antigravity. Whether you're setting up background tasks, tweaking your workspace utilities, or diving into deep research with subagents, these commands will help improve your daily workflow.

🔖 Bookmark this list to save time later.

Planning & Execution

/fast: Agent will execute tasks directly. Use for simple tasks that can be completed faster.
/goal: Run until the specified goal is completely finished.
/grill-me: Interview me to align on a plan.
/planning: Agent can plan before executing tasks. Use for deep research, complex tasks, or collaborative work.
/schedule: Run an instruction on a recurring schedule or as a one-time timer.

Customizations & Rules

/hooks: Manage hook configurations for tool events.
/keybindings: Set custom keybindings.
/learn: Reflect on recent successes or corrections to capture reusable skills or rules.
/mcp: Manage MCP servers.
/permissions: Manage tool permissions.
/skills: List available skills.

Subagents & Tasks

/agents: List available custom agents.
/tasks: View background tasks.
/teamwork-preview: Invoke a team of agents to autonomously tackle large projects. (Only available for Google AI Ultra subscription)

Workspace Utilities

/add-dir: Add a directory to the workspace.
/antigravity-guide: Provides a comprehensive guide, quick reference, and sitemap for Google Antigravity.
/artifact: View and review artifacts.
/btw: Ask a side question without interrupting the current task.
/changelog: Show release notes and changes.
/clear: Clear conversation and start a new one (alias: /new).
/codesearch: Search your entire codebase with live streaming results.
/config: Open settings panel (alias: /settings).
/context: Visualize current context usage.
/copy: Copy the last planner response to the clipboard (may require allowing clipboard access).
/credits: Displays third-party software licenses and attributions.
/diff: View uncommitted changes and per-turn diffs.
/effort: Adjust reasoning level to balance speed and depth.
/exit: Exit the CLI (alias: /quit).
/feedback: Submit qualitative feedback to improve the agent.
/fork: Create a branch of the current conversation at this point (alias: /branch).
/help: Show available commands and keybindings.
/logout: Log out.
/model: Set a model.
/open: Open a file or view opened/edited files.
/rename: Renames the active conversation thread.
/resume: Browse and resume past conversations (aliases: /switch, /conversation).
/rewind: Rewind conversation to a previous message (alias: /undo).
/statusline: Toggle the statusline.
/title: Toggle custom terminal window title.
/usage: View model quota usage (alias: /quota).

Get started with Antigravity: https://antigravity.google/

reddit.com

u/Kissthislilstar — 12 days ago

▲ 234 r/PromptEnginering+1 crossposts

DeepSeek “significant increase” for API pricing. It was too good to be true.

We already saw it with inworld tts, now it’s time for DeepSeek’s value capture. Thank you for all of your data and good publicity, we will now be significantly increasing cost.

u/Kissthislilstar — 12 days ago

▲ 205 r/PromptEnginering+3 crossposts

Uncensored Models

Are there any uncensored models? One of the issues I ran into with llama is that any discussion that sits adjacent to sexual or even political matters is basically blocked. Creative content ideas that walk the line between uncouth and commonly shared experiences/truth cannot even be discussed with the agent. I haven't explored taboo topics like talking about political figures or racism, but the models avoid touchy subjects in general. Has anyone found a solution?

reddit.com

u/Jorvex609 — 12 days ago

▲ 148 r/PromptEnginering+1 crossposts

Moving from OpenRouter to OpenAI cut my AI costs by 80% and made the same workload faster.. HOW???

I recently moved a production workflow for one of my startups from DeepSeek V4 Flash through OpenRouter to OpenAI’s Luna model.

It runs the same kind of work every day: generating structured stock briefs, enriching news, summarizing market data, and returning JSON for my app.

The difference has been kind of ridiculous:

DeepSeek through OpenRouter: roughly $0.60/day
Luna through OpenAI: roughly $0.14/day
Luna is also completing the workload noticeably faster

That’s around an 80% cost reduction, or almost 6x cheaper, with comparable output quality. I expected Luna to improve reliability, but I did not expect it to cost this much less.

It isn’t a perfectly controlled benchmark because token usage, caching, and daily workloads can vary. Still, the workload is similar enough that the gap surprised me.

Has anyone else seen this after moving production workloads away from OpenRouter? Could the difference mainly come from prompt caching, routing overhead, or DeepSeek producing more reasoning tokens?

I like OpenRouter for quickly testing different models, but direct OpenAI is currently faster, cheaper, and simpler for this workload. Kinda hard to argue with $0.14 versus $0.80 lol.

u/Kissthislilstar — 15 days ago

▲ 30 r/PromptEnginering+2 crossposts

Chao "🔥 Boogu-Image-0.1 just dropped — an open-source multimodal understanding and image generation model family! Trained on just 208M images with a budget of around $400K, ranks among the strongest open-source models across multiple benchmarks and blind evaluations" ➡️ wow is this hidden gem?

https://x.com/huang_chao4969/status/2084130198531006948

https://github.com/boogu-project/Boogu-Image

Resources are shared for discovery and are not independently vetted—please do your own due diligence.

New resources are added regularly — feel free to join the sub for updates.

u/Kissthislilstar — 15 days ago

▲ 271 r/PromptEnginering+2 crossposts

DeepSeek V4 Flash 0731 is now free in Cline

Hey everyone,

We’re making the new DeepSeek-V4 Flash 0731 free in Cline.

It scores 82.7 on Terminal Bench 2.1, which puts it surprisingly close to some of the strongest coding models available today.

We’ve already shared a deeper breakdown of the benchmark results, but the more interesting part now is seeing how it performs on actual codebases. We’re excited for you to try it and feel how far Flash models have come.

To use it:

Install Cline CLI: npm i -g cline
Open /settings
Select Cline as your provider
Choose deepseek-v4-flash

It’s completely free to use right now.

Curious to hear how DeepSeek-V4 Flash 0731 performs for the you all.

u/Kissthislilstar — 15 days ago

▲ 1 r/u_QVAC_Official+1 crossposts

We released VisionPsy-Nano, a SOTA 460M Vision model that leads its size class, compares favourably on 16 of 17 benchmarks & outperforms models up to 2.3x larger.

The Tether AI Research team has released a new model: VisionPsy-Nano, two ~460M-parameter vision-language models under Apache 2.0, with GGUF builds for local inference. The 460M is tuned for quality, the 460M-Flash for latency.

All numbers below come from a single VLMEvalKit run, with the eval configs and judge setup published so they can be reproduced.

Best in its weight class

It compares favourably on 16 of 17 benchmarks against every other ~0.5B VLM tested, with the highest overall score in its class: 62.3, against 59.6 for LFM2.5-VL-450M, 54.9 for the nanoVLM-460M-8k base and 52.5 for SmolVLM2-500M.

https://preview.redd.it/08wpa381w4gh1.png?width=2488&format=png&auto=webp&s=4c1d614546cc094887b6e38b63d954dd239f4c57

It tops all four capability areas, widest in reasoning (+7.4%) and visual perception (+4.6%).

https://preview.redd.it/6h1lqmn2w4gh1.png?width=2412&format=png&auto=webp&s=b17b249ddf1f4bce133349ac29d2c8a627cd336b

It beats models up to 2.3x its size

On ScienceQA (86.5), MM-IFEval (42.3) and POPE (87.9) it beats FastVLM-0.5B (759M), Qwen3.5-0.8B (873M) and InternVL3.5-1B (1061M) on all three. MM-IFEval is the one to examine: 42.3 against 36.9 for the next model in that group, and it measures whether the model actually follows the instruction it was given.

https://preview.redd.it/2s8dkzh4w4gh1.png?width=2552&format=png&auto=webp&s=b8edb9d29cc41d8c0c9b4b8bf7dd4bb011738d97

On real phones

Flash reads an image with 64 visual tokens instead of 1088 and keeps about 99% of the score. With quantized GGUF at 512x512 it reaches the first token in 0.3s on an iPhone 15, 2.6s on a Galaxy S25 Ultra and 6.1s on a Pixel 9, which is 11.9x to 25.1x faster than nanoVLM-460M and SmolVLM2-500M on the same runtime.

https://preview.redd.it/88yktz47w4gh1.png?width=2506&format=png&auto=webp&s=a739de802c62f63e89f18789876ad5ad5c6fcc92

How it was trained

Not by scaling data. A larger teacher VLM interrogated the model on real images, graded every answer with the image in view, and clustered the failures into a catalogue that steered the next round: 300 rounds, roughly 18,000 probes. It surfaced specific defects, including misread prices, chart values summed wrong, and repetition loops. Those became the training signal for a five-stage pipeline ending in TIES merging, so the capability specialists accumulate in one checkpoint at the same inference cost.

There are three ways to run it:

Transformers for full precision
quantized GGUF through llama.cpp for on-device
vLLM for your own GPU.

Model weights: https://huggingface.co/collections/qvac/visionpsy
Technical report: https://huggingface.co/blog/qvac/visionpsy

reddit.com

u/Kissthislilstar — 19 days ago

▲ 229 r/PromptEnginering+1 crossposts

I built a TikTok-style feed for discovering GitHub projects

I built: https://roamers.dev

GitHub is full of awesome projects and I love discovering things other people have built.

This sub is a great start, but it's often less tailored to my personal interests.
GitHub's UX for discovering projects is honestly just not good.

So I built it myself:

It's an endless feed of GitHub projects, free for you to roam around and discover. You can either sign in with GitHub and tune it to your interests, or just browse anonymously.

Feedback is greatly appreciated :)

u/Kissthislilstar — 26 days ago

▲ 147 r/PromptEnginering+1 crossposts

Besides Google Scholar, what are some underrated research sources you've discovered?

10 days ago, I shared list of research sources (post) here and received an enormous number of recommendations in the comments and DM.

In the end, I decided to check each one to see which of them would be useful and found some sources .

Some examples are:

OpenAlex — an open map of research papers, authors, institutions, and citations
Cochrane Library — one of the best sources for evidence-based medical reviews
Elicit — AI-powered literature review and paper discovery
ACM Digital Library — excellent for computer science and software engineering research
NASA EarthData — satellite, climate, and earth observation datasets
FRED — thousands of economic indicators and historical data series
BASE — indexes hundreds of millions of academic documents from universities worldwide
CORE — one of the largest collections of open-access research papers
ClinicalTrials gov — useful for finding ongoing and completed medical studies
Open Science Framework (OSF) — research projects, datasets, and pre-registrations across disciplines
Lens org — combines scholarly research with patent data, which is surprisingly useful
World Values Survey — public opinion and cultural data from countries around the world
GBIF — biodiversity and species occurrence data from across the globe
WIPO PATENTSCOPE — a goldmine if you're researching inventions, technology trends, or prior art
Our World in Data — one of my favorite sources for well-visualized data and research

The funny thing is that there is always the same number of popular websites (Google Scholar, PubMed, arXiv) which everyone uses and some useful sources no one mentions.

I am continuously adding recommendations to the research directory which I am building and now it has more than 230+ verified sources in various research areas.

Well, for anyone wondering, here is the link to it:

https://www.sourclip.com/resources/research-sources

There are even more sources that I am continuously finding out about.

Are there any good research sources that aren't that well known?

u/Kissthislilstar — 2 months ago

u/Kissthislilstar

Uncensored

Antigravity CLI Commands Cheat Sheet

Planning & Execution

Customizations & Rules

Subagents & Tasks

Workspace Utilities

DeepSeek “significant increase” for API pricing. It was too good to be true.

Uncensored Models

Moving from OpenRouter to OpenAI cut my AI costs by 80% and made the same workload faster.. HOW???

DeepSeek V4 Flash 0731 is now free in Cline

We released VisionPsy-Nano, a SOTA 460M Vision model that leads its size class, compares favourably on 16 of 17 benchmarks &amp; outperforms models up to 2.3x larger.

I built a TikTok-style feed for discovering GitHub projects

Besides Google Scholar, what are some underrated research sources you've discovered?

We released VisionPsy-Nano, a SOTA 460M Vision model that leads its size class, compares favourably on 16 of 17 benchmarks & outperforms models up to 2.3x larger.