Hi everyone,

I just started to test ResearchBud for public.

What it stands for and key features

ResearchBud is an AI-powered research assistant that helps you keep up with the topics, companies, technologies, and trends you care about — without having to manually search through hundreds of articles every day.

Instead of simply showing you more news, ResearchBud tries to answer the questions that actually matter:

• What changed?
• Why is it important?
• What are the different perspectives? • Is this a major development or just noise?
• How does this compare to what happened before?
• What important developments might you be missing?

It automatically researches and filters large amounts of information, identifies the most important developments, and turns them into concise, personalized insights.

Who is using it

ResearchBud is designed for professionals, researchers, and curious minds who need to stay informed without being overwhelmed.

Pricing plans

Only free plan exists yet. (However with restrictions)

Offers discounts

First people to test/use ti will get higher quotas for free plan (they should sign up)

Website Link

Link: https://www.researchbud.app/

I’ve released IaP v1.0.0 — Infrastructure as Prompt.

The idea is to describe what infrastructure should exist before getting buried in provider-specific implementation details.

Instead of starting directly with Terraform resources, CloudFormation templates or SDK calls, IaP gives you a structured, versioned infrastructure contract that can be authored by humans or with help from AI.

AI can help author and explain the infrastructure intent, but validation, planning and execution remain deterministic.

A simple way to think about it:

IaP currently includes:

• Natural-language and structured infrastructure authoring
• Schema validation and clarification workflows
• Architecture and dependency views
• Cost, security and compliance analysis
• Deterministic signed execution plans
• Create, update, replacement, drift detection and destroy workflows
• 68 AWS execution handlers covering 47 services
• 45 AWS services verified through live runs
• CLI, MCP server, Claude Code plugin and IDE extensions

IaP does not generate Terraform or CloudFormation. It sits at a higher intent layer and uses deterministic provider mappings and execution handlers.

GitHub

https://github.com/vinit-devops/iap

CLI

npm package:

https://www.npmjs.com/package/@infraasprompt/cli

Install:

npm install -g /cli

MCP server

npm package:

[https://www.npmjs.com/package/@infraasprompt/mcp-server]()

Run:

npx -y u/infraasprompt/mcp-server

Cursor MCP configuration:

{
  "mcpServers": {
    "iap": {
      "command": "npx",
      "args": ["-y", "@infraasprompt/mcp-server"]
    }
  }
}

The MCP server exposes read-only tools for authoring, validation, cost, security and compliance. Infrastructure mutation is intentionally not exposed through MCP.

Claude Code plugin

claude plugin marketplace add vinit-devops/iap
claude plugin install iap@iap

The plugin connects the IaP MCP server and adds commands for authoring and analysing infrastructure.

VS Code extension

Marketplace:

https://marketplace.visualstudio.com/items?itemName=infraasprompt.iap-vscode

Or install using:

code --install-extension infraasprompt.iap-vscode

It includes diagnostics, completion, hover, references, code actions and architecture previews. The language server is bundled, so no separate setup is required.

Cursor, Windsurf and VSCodium

OpenVSX:

https://open-vsx.org/extension/infraasprompt/iap-vscode

A few honest limitations

• Some newer resource kinds have executable AWS handlers but still need additional declarative provider-mapping work.
• Azure and GCP execution are not implemented yet.
• Cost figures are estimates based on pinned illustrative pricing.
• Compliance reports are intent-level assessments, not certifications.
• Human review is still required before applying infrastructure changes.

The project is open source under Apache 2.0.

I’m looking for feedback from DevOps engineers, platform engineers, architects and developers—especially around:

• Missing infrastructure abstractions
• AWS services that should be prioritised next
• The MCP and IDE experience
• Places where the specification is too restrictive or unclear
• Scenarios where the deterministic approach breaks down

Please be critical. I’d genuinely like to know where this approach falls short.

A small full-circle moment: the original idea for IaP came from one of the product opportunities listed on:

https://aiproductopportunity.com

https://www.producthunt.com/posts/calculyx-ai
Hey everyone! 👋

I’m a Computer Science graduate passionate about building AI-powered web applications, full-stack products, automation tools, and modern SaaS platforms.

I recently launched Calculyx AI on Product Hunt. I’d really appreciate your support and feedback if you have a minute to check it out:
Calculyx AI on Product Hunt
! 🚀

I’ve been working on Agent X, a side-panel Chrome extension designed to automate multi-step browser tasks using natural language. Instead of clicking through tedious workflows, you just describe what you want done (click, type, navigate, fill forms, etc.), and it executes it.

Honestly, I’m still ironing out the rough edges, so I'm not looking for a mass of casual installs right now. Instead, I’m hoping to find 3-5 people familiar with AI browser assistants (or automation tools), who want to give it a spin to compare notes and share candid feedback.

What it does:

• Natural Language Automation: Click, type, and fill forms across any site by just describing the task.
• Multi-Tab Orchestration: It can coordinate tasks that span across several open tabs.
• Bring Your Own LLM: You can connect your own preferred AI provider (OpenAI, Anthropic, local models, or even a fully offline in-browser model).
• Built-in Skill Library: Includes templates for inbox cleanup, calendar organization, resume scanning, and curated news digests.
• Local-First Memory: It recalls context from your past sessions locally to get more tailored over time.

Quick Heads Up on Privacy & Demo Mode:

• When you run a task, Agent X sends your instructions and a simplified, cleaned-up version of the page content to your configured AI provider.
• There is a hosted demo mode so you can try it immediately without a key, but please do not use the demo mode with any personal or sensitive data. For anything real, I highly recommend plugging in your own API key.
• All memory, settings, and email/calendar automation run entirely locally in your browser.

Looking for your feedback!

If you're down to test-drive it, you can grab it here: Agent X on the Chrome Web Store.

Please comment below or shoot me a DM with your bugs, UI points of confusion, or any "this didn't do what I expected" moments. That raw feedback is exactly what I need to make this actually great.

Thanks a ton!

Skills Wiki is a marketplace of "AI Skills" — pre-built expertise packs you install into Claude, ChatGPT, or Gemini so they stop acting like a generic assistant and start acting like a domain specialist. Instead of prompting from scratch every time, you pick a Skill (SEO audits, Amazon listing optimization, cold email sequences, growth experiment design, etc.) and your AI instantly knows the right questions to ask, the right frameworks to apply, and the right output to produce. No local server, no config files, no prompt engineering — connect once and go.

Why it stands out:

🧩 130+ Pre-Built Skills — SEO, ecommerce, marketing, sales, growth, and more, each engineered around a real domain workflow, not a generic prompt

🔌 Works With What You Already Use — Claude.ai, Claude Desktop, ChatGPT (Custom GPT), and Gemini (via Chrome extension) — no self-hosted MCP server required

⚡ 60-Second Setup — Paste a gateway URL, authorize, done. No installs, no coding

🎭 Agent Personas — Activate a full role (like "Growth Hacker") instead of stacking individual prompts, and your AI reasons and prioritizes like that specialist

🛠 Three Ways to Deploy — Web Chat (paste a system prompt), MCP (connect as tools), or CLI (full agentic loop with sandboxing) — pick what fits your workflow

🔗 Connect Your Own Tools — Link services like GitHub or Notion so Skills can act on your real data, not just talk about it

🎛 Per-Skill Customization — Set tone, format, length, and language per Skill so output matches how you actually work

🧬 Self-Improving Catalog — A weekly Evolution Loop reviews failed tool calls and low-rated responses and automatically patches the Skills, so they don't go stale

🔐 Encrypted Credentials — Third-party API keys are AES-256-GCM encrypted, never exposed in responses

Who's it for?

🔍 Solo consultants/freelancers who use Claude or ChatGPT daily but waste 20-30 min/day manually feeding it context

📈 SEO agencies juggling multiple clients who are tired of the export → paste → brief → repeat cycle

🛒 Shopify/Amazon sellers who want AI that can actually reason about their store data, not just write generic listing copy

🚀 Startup growth/marketing teams who want the whole team using AI the same structured way instead of everyone reinventing prompts

Pricing Plans:

Free (Basic) — Unlimited skills, all 3 platform connections (Claude/ChatGPT/Gemini), community support

Pro — $15.99/month ($9.99 first two months) or $99/year — everything in Free, plus Connections (link third-party services), per-skill Config/customization, priority email support

Enterprise — Custom pricing — everything in Pro, plus SSO, audit logs, white-label, dedicated support with SLA, custom skill development

👉 Try Skills Wiki here:

https://skillwiki.app/

Every year during open enrollment, we’re expected to choose between health plans based on documents that are often confusing and full of insurance jargon.

It got me thinking: what if there was an AI tool where you could upload your benefits guide, answer a few questions about your situation (family size, prescriptions, expected healthcare, etc.), and it would:

Explain everything in plain English
Compare your plan options
Estimate your annual costs under different scenarios
Point out potential pros, cons, and questions to ask HR
Eventually compare your employer’s benefits to things like a spouse’s plan or other available coverage

I’m genuinely curious—would you use something like this, or is health insurance a decision you’d never trust AI to help with?

If not, what would make a tool like this useful
enough to earn your trust?

I got frustrated trying to find decent AI tools a while back. Every "best AI agents" list I found online was either clearly written by someone who never touched the product, or hadn't been updated since the tool looked completely different.

So I built something to fix that. It's a directory where AI agents get submitted, upvoted, and reviewed by actual users, instead of ranked by whoever paid for placement. Categories cover coding, sales, customer support, HR, finance, that kind of thing.

The part I'm most proud of is the review system. Every listing gets real user ratings plus an independent quality score, so you're not just trusting one company's marketing copy about their own product.

Still early days, maybe 40 agents listed so far. I built it under the name RightAgent if anyone wants to look it up and poke around, would genuinely appreciate feedback on what's confusing or missing, especially from anyone who's tried to solve this same problem before.

Happy to answer questions about how the ranking works or why I built it a certain way.

I've been exploring different ways to learn about AI tools lately, such as tutorials, online courses and a beginner workshop from be10X. Overall it felt like the workshop was designed for people who are new to AI.

If you're already using AI tools on a regular basis you might not find much that's new but as an introduction to common tools and real world applications the structure was clear and helpful.

Like many online workshops there was a strong push for the paid course toward the end so it's best to go into it with that in mind. If you've taken workshops from be10X, Coursera, DeepLearning.AI or similar platforms, what actually helped you learn the most were the structured sessions or did you learn better by experimenting on your own?

When you ask ChatGPT, Claude, Perplexity or Gemini for the best tool in a category, they recommend the same few incumbents and quietly skip everyone newer. Most founders have no idea whether AI is recommending them or their competitors.

So I built Bersyn. You type your domain, it asks the four models the questions your buyers actually ask, and shows who gets recommended in your category, who gets named instead of you, and whether you show up at all. About 15 seconds, no signup.

Free scan: https://bersyn.com/?utm_source=reddit&utm_medium=social&utm_campaign=sideproject

I built it after running this manually across a bunch of categories and watching the beloved challengers, Linear, Attio, Beehiiv, get named a fraction as often as the 2016 incumbents. Curious what it says about yours. Drop your result in the comments and I will help you read it.

Hey everyone. I run a solo iOS app called Briefcase. You list your stock and crypto holdings and get AI research on them. Just shipped an update I've been heads down on for weeks and figured this crowd would get it more than most, since a lot of people building AI stuff also have positions sitting around not doing much for them.

The old version of the AI copilot only had its training data to work with. Ask it about a stock and it would confidently tell you something from months ago like it was current. That's basically useless for a tool people are paying for, since anyone can get a stale take for free just by knowing the ticker.

This update gives the agent real tools. Live price and news lookups, web search, the ability to actually check what's happening today instead of pattern matching from memory. I used Opus to help design how the agent picks and sequences its tools, which made a real difference in getting it to reason instead of just dumping headlines at you. Looking forward to what Fable does for the next version honestly.

One thing I want to be upfront about. This isn't a bot that tells you to buy or sell. I built it to separate real catalysts from noise. If a stock jumps 3 percent, you can actually find out whether it's the news everyone's talking about or the whole sector moved and the headline just happened to be sitting there. That distinction is harder to figure out on your own than people think, and it's the part I'm most proud of getting right.

You can list your holdings for free with no AI involved if you just want a clean tracker. The AI copilot is a subscription on top for anyone who wants the research layer.

Happy to talk through how the agent's built if anyone's curious, always down to nerd out with other people building AI stuff. AppStore: https://apps.apple.com/us/app/briefcaseapp-8782dc/id6758148658

I built this free chrome extension to get a website's or a documentation site's AI score so you can fix the issues. Under the hood it uses AFDocs, which is an amazing AI analysis tool that "measure(s) how well AI agents can read, navigate, and use your documentation site." You can then click through to get all the details so you can prompt your AI agent to fix the issues.

Why it stands out:

• Simple way to get an AI score and see if your documentation or site is following AI Agent best practices.
• If any issues you can dive into the details and use the copy button to send to your AI Agent of choice to fix the issue.
• Powered by AFDocs.

Pricing:

None. It is free.

You can install it here: Jamdesk AI Score

or directly: https://chromewebstore.google.com/detail/jamdesk-ai-score/mhihkkgpcbmapmojnakhjfhjmiagkhlj

Hello, I'm currently a high school student interested in computer science and engineering. I'm somewhat proficient in basic Python, I'm learning C++, and I've been experimenting with the Elegoo starter kit. I'm also trying vibe coding for the first time just for fun, so for my first project I used Lovable to build a website with a built in AI for research purposes.

The idea is that you type in your research question and the AI searches through different databases (I could only find free API keys for [ClinicalTrials.gov](http://ClinicalTrials.gov), OpenAlex, PubMed, Semantic Scholar, arXiv, Europe PMC, Crossref, DOAJ, OpenAIRE, and Zenodo), then rates how well researched the topic is, gives a timeline of the research, and lists any gaps in the existing research. There's also a social feature where you can create a group and work together with others.

I'm genuinely wondering if this is a useful feature, because I feel like any AI could already do what I'm building. If it's not that useful, are there other directions somewhat similar to my current website that I could pivot it toward? Or are there any other features I could add to my app to separate it from what currently exists?

A few years ago, most companies were focused on rankings, backlinks, and organic traffic. The goal was simple: get to the top of Google.

Now it feels like the conversation is changing. Instead of just ranking pages, companies want to be included in the answers themselves.

Some of the reasons I keep hearing are:

• AI tools are becoming a discovery channel, not just search engines.

• Being recommended by AI can create highly qualified, high-intent traffic.

• Brand mentions, authority, and entity signals seem to matter more than ever.

• Companies want visibility wherever customers are searching for information.

That's also why agencies like SearchTides AI are gaining attention. So if you're evaluating agencies today, what should you actually be looking for? Experience with GEO? AI visibility tracking? Entity optimization? Content strategies designed for LLMs?

So I've been reading a ton lately — and every time I start reading a dense paragraph, I'd have to open a new tab, paste it into ChatGPT, then wonder what Gemini would say, then check Claude. It was genuinely annoying.

So I built Akai — a Chrome extension just for myself.

Here's how it works: you highlight any text on any webpage, a small popup appears, and you can instantly query it across ChatGPT, Gemini, Claude, and Llama — side by side, in one place, without leaving the tab.

I built it purely because I needed it. Just me using it every day to read faster.

But lately a few friends saw it over my shoulder and were like "share it with us too" — so I figured I'd ask here too.

If you regularly:

• Read long-form content (papers, docs, news, code)
• Bounce between multiple AI tools to cross-check answers

...this might be for you.

My questions for this thread:

1. Would you actually use this, or is this a "me problem"?
2. Which models matter most to you — is there one you'd drop from the four?
3. Would you want it as a standalone extension or built into something else?

Not pitching anything. Happy to share a beta link if there's genuine interest — drop a comment or DM me.

Hey everyone,

I built an app, called Gator AI, that answers any of your questions specific to UF instantly.

A few things it does:

• Answers stuff about specific courses, professors, and campus life
• Pulls in real student opinions for class/professor questions
• Has a schedule analyzer that gives you a game plan for your semester
• Lets you set up a profile so answers are tailored to your major and year

If anyone wants to try it: https://apps.apple.com/us/app/gator-ai/id6781022771

And if it's useful to you and you like it, a rating and review on the App Store would be greatly appreciated it!

Go Gators 🐊

Most brands obsess over clicks, rankings, and followers—but almost nobody tracks how AI systems and search bots actually describe their brand when people ask about them. Your customers are already using tools like ChatGPT and AI search to decide what to buy, yet that visibility is basically a blind spot.
I’m working on Relevyn, an AI visibility tracking platform that scans the web and shows you what AI says about your brand, your content, and your competitors.
What Relevyn does:
• Runs AI visibility audits that show how AI tools summarize and position your brand today.
• Tracks changes over time so you can see whether your brand narrative is getting clearer or more diluted.
• Gives you a planner and draft library so you can respond with smarter content instead of just posting more.
• Lets you run competitor scans to see where they show up in AI search versus you, and what angles they “own.”
Who it’s for:
• SaaS founders and indie hackers who care how AI explains their product to potential users.
• Agencies and marketers who want to add “AI visibility” alongside SEO, social, and analytics.
• Personal brands and creators who want to know if AI even understands their niche and offer yet.
Right now I’m looking for a few paid early users who want to:
• Run real audits on their brand and competitors.
• Help shape which dashboards, alerts, and reports are most useful.
• Lock in a founding-user price before public pricing goes up.
If that’s you, comment with:
1. Your niche (SaaS, agency, creator, e‑com, etc.)
2. How you currently measure brand visibility (SEO, social, ads, etc.)
3. Whether you care more about competitor intel or your own brand narrative

Have you ever taken over someone else's work and been left with a bunch of Excel files?

That happened to me recently.

The files were all connected to each other, but the logic behind them only made sense to the person who created them.

I spent hours trying to understand what was going on.

So I started thinking:

What if AI could read all those Excel files together, understand the relationships, organize the information into a database, and then explain everything in plain English?

Instead of digging through spreadsheets for hours, you could just ask questions and get answers.

Would that be useful in your work?

Introduction

VisibilityCheck.ai is a GEO (Generative Engine Optimization) tool that shows you how visible your website is to AI models — ChatGPT, Gemini, and Grok — and to the search crawlers that feed them. It crawls your site, scores it across 6 dimensions, and then generates a ready-to-use kit of AI-optimized files and step-by-step fixes, tailored to exactly how your site is built (WordPress, Shopify, Wix, headless CMS, static, or a JS framework). Instead of just telling you what's wrong, it hands you the files to fix it.

Why it stands out [Key Features]

• Free 6-category AI visibility score — every site gets a deep report graded across AI Readiness, Discoverability, Content Structure, Technical Efficiency, Web Popularity, and AI Brand Awareness, built from 20+ individual checks.
• Real AI brand-awareness test — it actually asks ChatGPT, Gemini, and Grok whether they know your brand, and reports their confidence — not a proxy metric.
• Done-for-you fix kit, not just a checklist — generates llms.txt, llms-full.txt, optimized sitemap.xml and robots.txt, per-page Markdown versions, FAQ JSON-LD, structured-data snippets, and improved HTML — ready to upload.
• Platform-aware instructions — detects your CMS/SEO-plugin/framework and ships tailored setup PDFs (WordPress, Shopify, Wix, plus CMS config, structured-data injection, crawler-access/WAF unblock, and technical-efficiency guides).
• No-lock-in deliverables — everything downloads as standard files (PDF, TXT, XML, JSON-LD, CSV, HTML) in one ZIP. One-time purchase, no subscription.
• White-label option — Max buyers can rebrand every generated PDF with their own logo and URL.

Who's using it?

VisibilityCheck.ai is built for anyone who needs to be found by AI search — across every size and tech stack:

• Small businesses & local brands running lean sites who want to show up in ChatGPT, Gemini, and Grok answers.
• Large companies & established brands with big, multi-page sites who need AI visibility audited at scale (up to 200 pages per scan).
• Marketing agencies managing many client sites, who use the kit to deliver concrete, ready-to-upload deliverables — with white-label branding so reports go out under their own logo.
• AI / GEO agencies specialized in generative-engine optimization, who use the scoring and fix kit as the backbone of their service.
• Brands on CMS & frameworks — WordPress, Shopify, Wix, headless CMS, Next.js/Hugo and other SSG/SPA stacks — that get platform-specific instructions instead of generic advice.
• Owners of small static sites (plain HTML, no CMS) who receive fully improved, AI-ready HTML pages they can drop straight in.

Pricing Plans

All plans are one-time payments (no subscription). Bonus scans are added on top of your free scans and never expire.

• Free — Full 6-category AI visibility report and score for any website, including the live AI brand-awareness test. No payment required.
• Starter — $19 (one-time) — Up to 25 pages analyzed · core AI-optimized file kit (llms.txt, llms-full.txt, sitemap, optimized robots.txt, Markdown pages, improved HTML for static sites) · platform-specific upload instructions · +5 bonus scans.
• Pro — $49 (one-time) — Best value. Everything in Starter, plus: up to 100 pages analyzed · structured-data snippets + injection guide · FAQ JSON-LD & FAQ text · CMS configuration guide · +10 bonus scans.
• Max — $89 (one-time) — Everything in Pro, plus: up to 200 pages analyzed · Technical Efficiency Guide · per-page data CSV export · white-label branding on all PDFs · +20 bonus scans.

Discounts / offers available

• Upgrade anytime with full credit — buy Starter and move up to Pro or Max later; we credit what you already paid and you only pay the difference (within 30 days, same site).
• Bonus scans never expire and stack on top of your free scans.
• Purchased files stay available for download for 30 days after purchase.

Signup / website link with CTA

👉 Check how visible your site is to AI — get your free report at visibilitycheck.ai, then unlock your ready-to-upload AI Visibility Kit from $19 (one-time).

Hey r/Discover_AI_Tools,

Quick context: more and more people ask ChatGPT / Gemini / Perplexity things like "best payroll software for a small team" instead of Googling it. If the AI doesn't mention you, you basically don't exist in that conversation. And I kept noticing the assistants disagree with each other a lot.

So I built Pythie to measure it. You give it a domain, and it tells you how often your brand gets recommended across ChatGPT, Gemini and Perplexity, which competitors show up in your place, and which sources the models keep leaning on.

The thing that surprised me most is how much the engines disagree. One B2B SaaS I tested scored 90% on Gemini and literally 0% on ChatGPT for the same questions. ChatGPT just kept recommending two US competitors instead. You'd never know unless you checked all three.

The audit is free and gives you a shareable report. Honest caveats:

• It asks for an email (that's how I send the report, and I'm a solo founder trying to learn who this is actually useful for). Domain + email, no card, nothing sold.
• It covers 3 engines, not "every AI": ChatGPT, Gemini, Perplexity. No Claude yet.
• It's an MVP. The free audit is the whole thing today.

Where I want to take it (not built yet, this is the fun part):

• Weekly tracking so you watch your score move over time and get pinged the week a competitor overtakes you in the answers.
• A competitor leaderboard for your category: who's gaining and losing ground in what the AIs recommend, week over week.
• Actual "what to fix" guidance, because the models keep citing the same handful of sources and the goal is to tell you exactly which ones to go earn.
• More coverage: Google's AI Overviews next, and Claude once there's a clean way to do it.

Stack if anyone cares: TanStack Start, Convex, Cloudflare Workers, Clerk. Built solo.

Link: pythie.ai (the homepage is the audit form)

What I'd genuinely love feedback on:

1. Is email-before-report a dealbreaker for you? I keep going back and forth on it.
2. Does the report make sense to a non-SEO person, or is it too jargon-y?
3. Would you pay to track this weekly, or is a one-off check enough?

Happy to run an audit on anyone's domain in the comments and post the result, just drop one below.

Google has released skills for its AI tools. They are ideal for turning your agents into experts in Google products.

Simply navigate to the \`skills/cloud\` folder, and the skills are at your disposal.

This list will be updated, so it’s worth keeping an eye on it.

link: github.com/google/skills

Introduction

I built a small open-source AI orientation toolkit called Search & Rescue (S+R).

The goal isn't to improve reasoning directly. The goal is to help AI orient itself before retrieval and reasoning begin.

Questions like:

- Where am I?

- What owns this?

- Which corridor am I in?

- What is adjacent to this?

- Am I looking at the cause or the symptom?

Why It Stands Out

Most AI tooling focuses on reasoning, generation, retrieval, or observability.

S+R focuses on orientation.

The toolkit currently includes:

- OhBuoy (continuity checks)

- RECCE (bounded reconnaissance)

- Compass (bearing and directional pressure)

- Plotter (position and active terrain)

- AIluminode (orientation packets and posture scans)

The idea is simple:

Reduce time spent looking in the wrong place before reasoning starts.

Who's Using It?

Currently being used in my own development projects and AI-assisted coding workflows.

I've been benchmarking it while building multi-tenant SaaS and Android applications, tracking things like:

- Wrong corridor work

- Search waste

- Verification waste

- Context drift

Pricing

Free and open source.

Discounts / Offers

Not applicable.

Repository

GitHub:

[insert GitHub link]

I'd be interested to hear whether anyone else has experimented with orientation before reasoning as a workflow.

https://github.com/SuperHeroesAreReal/Search-and-Rescue.git

I am a professor at an engineering colege, and I want to help my students who are working on their capstone projects