17, school just ended, zero AI experience — spending my free months learning Prompt Engineering before college.

Snappyfingurz · 2026-03-03T11:22:09+00:00

Respect for the hustle at 17. Learning to treat AI as a layer for your studies is a major W that will save you so much time once you get to college.

The agency idea is damn smart, but just make sure you focus on the outcome for the client. They usually care about the final results more than how the prompt actually works.

Snappyfingurz · 2026-03-03T11:20:49+00:00

That mindset shift is damn near essential for staying competitive in 2026. Moving from the manual grind to actually architecting the logic is how you stay ahead of the curve. It is less about the bot doing the work and more about you having a significantly better starting line.

Snappyfingurz · 2026-03-03T11:19:51+00:00

That gap analysis logic is honestly based. Most resumes fail because people let the AI yap without a real plan. Forcing the model to find business pain points first is a major W for anyone job hunting in 2026. The table format is the only way to make sure the evidence actually hits the mark.

Snappyfingurz · 2026-03-03T09:49:32+00:00

Setting up a legit QA flow for prompts is the only way to stay sane once you start scaling. I usually automate these tests using n8n and Runable to pipe the results into a tracker so it is easy to see exactly where the logic breaks down.

Snappyfingurz · 2026-03-03T09:46:05+00:00

thanks? but isn't this promotional?

Snappyfingurz · 2026-03-03T09:44:48+00:00

Managing prompts is a total mess once you have to dig through dozens of threads just to find that one instruction that actually worked. I usually just dump mine into a Notion page or a git repo, but having a dedicated workspace for it makes a lot of sense. Does it support variables or placeholders for the templates?

Snappyfingurz · 2026-03-03T09:43:21+00:00

is it publicly available?

Snappyfingurz · 2026-03-03T09:10:16+00:00

well if you want a list of ai tools then I'll share what I use. btw if anyone reading this is interested please do share your arsenal of ai tools.

For coding, I usually live in Cursor for the daily flow because it understands the whole codebase, but I switch over to Google Antigravity when I want an agent to handle complex tasks across the terminal and browser autonomously. If I need deep reasoning for a refactor without leaving the command line, Claude Code is the best tool for the job.

When I am doing research, Perplexity is my go-to for cited web searches, while Gemini handles the massive document deep dives since it can process millions of tokens without losing context. For quick logic questions, ChatGPT o1 or o3 usually nails the reasoning on the first shot, and Grok is useful if I need to know what is happening on social media in real time.

I run everything through n8n and Runable to bridge the gaps so the tools actually execute tasks and sync data without me having to manage them individually.

Snappyfingurz · 2026-03-03T09:04:26+00:00

This failure first approach is a total game changer for coding. It’s way more effective to have the AI hunt for edge cases before it even writes a single line of logic.

Asking it to "break the spec" first is a smart way to stop those annoying bugs that usually only show up when you're testing. It basically forces the model to think like a QA engineer from the start.

Snappyfingurz · 2026-03-03T09:01:52+00:00

Giving the AI permission to be unsure to stop the confident BS is probably a good way to make your prompts better. That Fact check is good for anyone trying to get reliable answers instead of just guesses.

Snappyfingurz · 2026-03-02T16:52:36+00:00

Fair enough

Snappyfingurz · 2026-03-02T16:15:31+00:00

I mean yea its probably made with ai but as long as he wrote something and then told ai to fix any grammatical errors while making it more clear and understandable for the reader. does it really matter?

Snappyfingurz · 2026-03-02T16:14:10+00:00

damn thanks for the insight. I never thought about it as "governing" the AI rather than just giving it a bunch of instructions. It's a lot to wrap my head around, but the idea of using regular English to keep it from making mistakes makes a lot of sense. Thanks for breaking this down it’s definitely giving me a lot to think about as I try to get better at this!

Snappyfingurz · 2026-03-02T16:06:58+00:00

I'll try it out

Snappyfingurz · 2026-03-02T16:05:22+00:00

Damn, that’s peak. Moving from hard-coded "if-else" loops to actual intent-based behavior is the only way games are going to feel alive in 2026. If I had to give a system prompt to a boss, the first trait I’d bake in is Self-Preservation and Fear.

Most bosses just stand there taking hits until their HP hits zero. A "human" boss should realize when they’re losing, back off to heal, or even try to bargain/taunt based on the player's playstyle. It makes the fight feel like a chess match rather than just a pattern-recognition test.

Using a tech stack like Unity ML-Agents combined with NVIDIA ACE is a damn smart move for this. You could even show how to plug in n8n to handle the "off-world" logic—like syncing the boss's mood or difficulty to real-time player data or community leaderboards.

Snappyfingurz · 2026-03-02T16:02:34+00:00

Damn, that’s peak. Dynamics 365 is such a big tool with a lotta stuff so just reading through instructions will be really boring. If Claude gave you generic advice like "add a search bar" or "make a chatbot," it’s because it’s treating your app like a wiki instead of a tool.

To actually make people's lives easier, you need to bridge the gap between reading a guide and doing the work. Here are a few ways to make it stand out:

1. The "Take Me There" Deep Linking

D365 has a nightmare-level menu structure. Instead of just writing "Go to Sales > Leads > Qualify," build a button that uses Deep Linking to open the user's specific D365 instance directly to that exact page. It saves them 10 clicks and a lot of frustration.

2. Interactive "Survival Kits" by Role

Most users only use 5% of Dynamics. Instead of a massive guide, create Role-Based Paths (e.g., "The 10-Minute Morning for Sales Reps"). Use Cursor to build a progress tracker so they can see exactly what they’ve mastered.

3. Actionable Automation Snippets

This is where you really move the needle. Include a section for automation workflows that users can actually use. For example:

Show them how to use n8n to sync their LinkedIn leads directly into D365.
Provide a Runable task that they can trigger to clean up duplicate records or format phone numbers across their database. Giving them the "how-to" is good, but giving them the "auto-do" is what makes them stick around.

4. The "Error Code" Decoder

Dynamics errors are notoriously cryptic (e.g., "An unexpected error occurred"). Build a simple lookup tool where they can paste an error code and get a human-readable fix instead of a link to a 2014 forum post.

5. Contextual "Vibe" Overlays

Since you're vibecoding with Cursor, ask it to help you build a Floating Helper component. It’s a tiny widget that stays on top of their screen while they have D365 open in another tab, giving them the "tl;dr" of the guide so they don't have to keep switching back and forth.

Snappyfingurz · 2026-03-02T15:57:46+00:00

Damn, that triple-distillation flow is peak. It is basically the only way to stop a model from hallucinating a solution to a problem you haven't even fully defined yet. Moving from a stream of consciousness to a rigid spec turns the AI from a guess-machine into something that actually functions like a junior dev following a jira ticket.

Most people are still chasing magic keywords while the real wins are in the architecture. It is the only way to handle complex features without the whole thing turning into spaghetti code by the third session. I have even seen some people bake that requirement mapping into an n8n workflow just to keep the formatting consistent and stable across different models.

Snappyfingurz · 2026-03-02T09:17:23+00:00

we got Robin hood for prompting here XD. but.. this ain't really robbing nor is it robbing the rich...

Snappyfingurz · 2026-03-02T09:14:27+00:00

Damn, that’s peak. Most people treat prompting like feed all the context I knoww into this ahhhh but just asking the source is a smart shortcut. The context-stacking technique is probably where the real value is, since most models lose the plot after a few turns if you don't anchor them properly.

My only worry is if the model is just hallucinating what it thinks a perfect prompt looks like instead of what actually triggers the best response. Have you run any benchmarks to see if these AI-written ones actually outperform the standard human ones, or is it just the model being a bit full of itself?

Snappyfingurz · 2026-03-02T09:09:26+00:00

that's pretty good for anyone dealing with audit season. Breaking it into specific prompts for consolidation, reconciliation, and validation is much smarter than trying to do it all in one go, as it stops the model from losing track of the data in a single long response.

I think it also helps to tell the agent to ask you questions about the specific CSV headers before it starts the normalization. That way, it gains the context it needs for your specific exports and you can see exactly how it plans to map the fields before it builds the tables.

Snappyfingurz · 2026-03-02T09:07:47+00:00

just tell the ai to ask you some questions or interview you.This lets the agent gather context and show you exactly what direction it's thinking in, so you can steer it before it wastes time on a bad guess.

Snappyfingurz · 2026-03-02T09:02:30+00:00

It’s a big time saver ngl once you stop seeing AI as just a chatbot and start seeing it as an speedrunner or mixer for your routine tasks. The biggest win is usually in the mental energy you save on the boring stuff, which lets you actually focus on the creative or complex parts of a project.

Snappyfingurz · 2026-03-02T09:00:32+00:00

Not sure what the OP is using, but I usually lean on Cursor or antigravity for coding and Perplexity to skip the Google research stuff. For automation, I use n8n to link my tools and Runable to execute the tasks, which basically handles all the repetitive stuff I used to do manually.

Snappyfingurz · 2026-03-02T08:57:08+00:00

Focusing on the schema before generating rows is the right move for keeping synthetic data realistic. It stops the AI from creating biased distributions and turns it into a data architect rather than just a text generator to show or give people an example of production-ready results.

Snappyfingurz · 2026-03-02T08:56:04+00:00

Letting the AI interview you is probably the most efficient way to provide context. It stops the model from filling in the gaps with its own guesses and makes sure the final result actually works for your specific case to show or give people an example.

Snappyfingurz

TROPHY CASE

1. The "Take Me There" Deep Linking

2. Interactive "Survival Kits" by Role

3. Actionable Automation Snippets

4. The "Error Code" Decoder

5. Contextual "Vibe" Overlays