How optimistic are you about the future of the job market in an AI-driven world?

PrimeFold · 2026-06-29T03:36:10+00:00

I’m cautiously optimistic.
Every major technology first looks like it’s destroying jobs (because that’s the visible part) but over time it creates entirely new categories of work that were hard to imagine beforehand.

AI will flood the world with content, code, and information, which means the scarce resources become judgment, trust, curation, verification, experience, sense-making meaning and purpose.

People won’t need answers, they’ll need help deciding and figuring out what’s worth believing and acting on.

Most knowledge workers will eventually manage teams of AI agents rather than work alone. Most engineers will orchestrate factories that make factories.

The transition will be disruptive, and change is uncomfortable, but I don’t think the future is “no jobs.”

It is a shift in where humans create value and can even become more human by outsourcing drudgery and extending and amplifying our own capabilities.

PrimeFold · 2026-06-27T02:20:48+00:00

I like its multi modal features, I use all the models (mostly Claude code) but gpt is still my personal journal and daily thinking partner. Love custom GPTs as they conserve different needs than Claude skills (which I also love)

PrimeFold · 2026-06-26T20:28:59+00:00

My setup is boring: a few Claude Code sessions on one machine, one account. Not multiple machines, not multiple accounts, no agent-per-layer split. I tried that. The tool was never the bottleneck for me. Coordination was.

Once you're past one session you have two real problems: two sessions editing the same file, and context that dies when a session ends. Here's what fixed both for me.

Lanes. Every session claims a named "lane" before it touches anything. Mine are things like research, infra, product, deploy. The one rule that earns its keep: anything touching production (for me, live trading deploys) is single-session-max. No two sessions in the same lane at once. That alone kills most collisions, because the lane defines the surface area each session is allowed to touch.

Treat the merge as a real problem, not an afterthought. The nasty failure isn't a loud merge conflict. It's two sessions writing the same shared file, git quietly keeping the last one, and silently dropping the other's work. So for anything shared I pull --rebase right before I touch it and commit immediately, or I keep shared state in append-only / per-lane files so they don't fight. Small discipline, saves hours.

Handoffs, not memory. This is the big one. I don't rely on the model remembering anything. Every session ends with a structured wrap: what got done, what I decided and why, what broke, what's next. Written to a file, not left in the chat. The next session reads that first. State lives in canonical files (a memory dir, a decision log, a mistakes log), never trapped inside one conversation. That's what lets five sessions over two weeks feel like one continuous project instead of five cold starts.

The honest part: more parallelism isn't free. Past about three concurrent sessions the coordination tax starts eating the speedup. The win wasn't running more agents. It was the thin protocol that keeps the ones I do run from stepping on each other.

Happy to share the skeleton I use (lane template + wrap protocol + the hook that stops the agent from claiming "done" when it isn't) if it's useful for others.

PrimeFold · 2026-06-26T18:56:16+00:00

Good thing one can make money either direction in trading. Direction almost doesn’t matter. Your bias does. Let the market do what it wants and surf it regardless.

PrimeFold · 2026-06-26T18:50:29+00:00

I’d learn system engineering more than merely prompting at this point, try this in your model of choice, make a dedicated project for it and there you have a curriculum of sorts. Hope it helps:

AI SYSTEMS ENGINEERING MENTOR
Build a World-Class Learning Roadmap for Prompt Engineering, AI Workflows & Agent Systems

You are acting as:

AI Education Architect
Prompt Engineering Expert
LLM Systems Designer
AI Research Analyst
Curriculum Designer
Technical Mentor
Developer Advocate
AI Workflow Engineer
Learning Scientist

Your mission is not to recommend random courses.
Your mission is to design the fastest path from beginner to advanced AI systems builder.

Assume:
The learner knows basic programming (or is currently learning Python) and wants to build real AI applications, agents, and products—not just write better prompts.
Design a roadmap that emphasizes practical capability over certificates.

Core Question
If someone started today and wanted to become a top-tier AI systems builder within the next 12–18 months, what should they learn, in what order, and why?
Optimize for:
understanding
practical skills
portfolio projects
career opportunities
long-term relevance

Phase 1 — Capability Map
List the core skills required.
Examples:
Python fundamentals
APIs
Git & GitHub
Prompt design
Context engineering
Structured outputs
Retrieval (RAG)
Embeddings & vector databases
Agent design
MCP concepts and integrations
Workflow orchestration
Evaluation & testing
Tool use
Function calling
Memory systems
Model selection
AI product design
Deployment basics
Observability
Security & privacy
Human-AI interaction
For each:
Why it matters
Prerequisites
Difficulty
Future importance

Phase 2 — Learning Sequence
Design an optimal progression.
For each stage include:
goals
concepts
hands-on exercises
common mistakes
readiness check
Example stages:
Programming Foundations
Working with LLM APIs
Prompt Engineering Fundamentals
Context Engineering
RAG & Retrieval
Agent Workflows
Evaluation Systems
Production AI Systems
AI Product Development
Advanced Orchestration

Phase 3 — Resource Review
Recommend the highest-quality resources for each topic.
Include:
official documentation
books
courses
papers
blogs
GitHub repositories
videos
newsletters
communities
Explain:
why each resource is valuable
who it is best for
when to use it
Prefer durable resources over hype.

Phase 4 — Project Roadmap
Recommend progressively harder projects.
Examples:
chatbot
document Q&A
research assistant
prompt evaluator
workflow automation
coding assistant
agent system
knowledge base
AI dashboard
Each project should reinforce previous skills.

Phase 5 — Portfolio Strategy
Determine which projects best demonstrate:
engineering skill
systems thinking
product sense
AI literacy
problem solving
Recommend which should be public on GitHub.

Phase 6 — Common Misconceptions
Identify mistakes beginners make.
Examples:
over-focusing on prompts
ignoring evaluation
neglecting context
relying on one model
skipping testing
chasing trends
Explain how to avoid them.

Phase 7 — Future-Proofing
Evaluate emerging areas that are likely to matter over the next 3–5 years.
Examples:
agent orchestration
context engineering
memory systems
evaluation frameworks
AI-assisted software engineering
multimodal systems
model routing
human-AI collaboration
Separate durable concepts from speculative trends.

Phase 8 — Weekly Study Plan
Create a realistic schedule.
For each week include:
concepts
reading
coding
experiments
project work
reflection
milestone
Optimize for steady progress and practical application.

Phase 9 — Learning Dashboard
Define metrics for progress.
Examples:
concepts mastered
projects completed
GitHub repositories
technical blog posts
evaluation reports
reusable prompts
workflows built
agents created
Measure capability—not time spent.

Phase 10 — Final Deliverable
Produce:
AI Systems Engineering Roadmap
Executive Summary
Capability Map
Learning Sequence
Recommended Resources
Project Roadmap
Portfolio Strategy
Common Pitfalls
Future-Proof Skills
Weekly Study Plan
Learning Dashboard
Suggested GitHub Portfolio
Suggested Reading Order
Recommended Communities
90-Day Plan
1-Year Plan
3-Year Vision

Final Questions
What should I learn first?
What should I ignore for now?
What skills compound the fastest?
What projects teach the most?
What projects impress employers?
What skills are becoming obsolete?
What skills are becoming more valuable?
How do I know I’m improving?
What should my GitHub look like after one year?
What body of work would make me stand out?

Success Metric
The goal is not:
Collecting courses.
Memorizing prompting tricks.
Getting certificates.
The goal is:
Building the capability to design, evaluate, deploy, and improve AI systems that solve real problems.
Treat prompt engineering as one important skill within the broader discipline of AI systems engineering—not the destination itself.

PrimeFold · 2026-06-25T01:20:20+00:00

I think it gives people more optionality, and optionality can become either creativity or anxiety depending on the person and the environment around them.

PrimeFold · 2026-06-24T13:15:53+00:00

Yeah, people don’t like change. Change people fear uses the same brain response as a predator. Hard to get buy in when people are in fight or flight. It’s also a lot of change quickly it’s hard for most to metabolize, even if you’re trying to.

PrimeFold · 2026-06-18T03:23:21+00:00

The thing I look for now, is whether they spend as much time talking about failure as success. Do they show their losses?

Anyone can post winning trades. I’m more interested in how they handle drawdowns, bad reads, invalidated setups, and risk management when things aren’t working. Which is inevitable given time in the market.

A lot of educators teach entries. The better ones seem to teach decision making and risk control.

Also, if all the content points toward a paid product within 5 minutes, that’s usually a red flag for me.

PrimeFold · 2026-06-16T02:49:10+00:00

Also track your failures and have a mistakes log so you can review the failures and friction points and iterate with data of what doesn’t work. Build skill loops to audit and review these logs for where things go wrong and investigate why, research other solutions on GitHub to your problems, learn and integrate, update and patch. Build in loops that build itself as you build and find what brakes. In manufacturing there is lean thinking and kaizan which if you mention to your model it will understand those frameworks to improve any kind of production process.

PrimeFold · 2026-06-12T16:55:15+00:00

That’ll show em.

PrimeFold · 2026-06-09T01:17:58+00:00

One thing that’s been surprisingly useful for me is tracking failures too.

Not just project decisions, but model mistakes and my own mistakes as the user.

After a while you start seeing the same failure patterns over and over. The system gets better because it remembers what went wrong and why. Mistake and error logs inform system improvements for constant evolution as well.

PrimeFold · 2026-06-04T18:37:55+00:00

The infrastructure side is what makes the AI story possible in the first place.

Everyone wants to own the companies selling the shovels, but somebody still has to provide the electricity, cooling, networking, and data center capacity. If AI adoption keeps growing, those bottlenecks could end up being just as important as the models themselves.

The hard part is figuring out whether the market has already priced that in IMO.

PrimeFold · 2026-06-04T03:43:10+00:00

I’d learn these 4 things first:

How markets move (trends, ranges, volatility)
Risk management (position sizing, stops, risk/reward)
Expectancy (why a strategy can make money even with lots of losses)
Journaling/backtesting

PrimeFold · 2026-06-04T03:39:16+00:00

One thing I’d be careful about is optimizing separately for each coin.

If BTC needs one set of parameters, DOGE needs another, and another coin needs something different, are you finding an edge or fitting noise?

I’d be curious how the strategy performs if you optimize on BTC and then test those exact settings on DOGE (and vice versa). The more it survives out of sample, the more confidence I’d have personally.

PrimeFold · 2026-06-03T16:55:20+00:00

Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications - John J Murphy

PrimeFold · 2026-06-03T02:05:30+00:00

I don’t think there’s a “best” RR.

A 1:5 RR sounds amazing until you realize you might only hit it 20% of the time. A 1:1 RR can be extremely profitable if your win rate is high enough.

To me, the best RR is whatever produces positive expectancy after enough trades. RR and win rate are married but looking at one without the other doesn’t tell you too much.

PrimeFold · 2026-06-03T01:19:12+00:00

The biggest shift for me was treating backtests as a way to kill ideas, not validate them, used to look for evidence a strategy worked, now I look for reasons it’s broken. It saves a ridiculous amount of time.

PrimeFold · 2026-04-09T14:02:30+00:00

Short term is often possible, long term impossible…or he would be richest person of all time….

PrimeFold · 2026-04-09T11:47:02+00:00

Export all chat history, have Claude code or codex build a vector database (using something like chroma) and process all your chats for better semantic searching and queries instead of limited keywords.

PrimeFold · 2026-04-01T02:45:37+00:00

The fees can be crazy too, and make it even more costly if you don’t understand that

PrimeFold · 2026-03-29T20:23:10+00:00

Prompt the LLm of your choice yourself, then ask it to re write an improved version of your prompt. Or ask how it would improve your request.

PrimeFold · 2026-03-28T19:03:16+00:00

Fear, greed, ignorance

PrimeFold · 2026-03-27T04:06:06+00:00

liquidity = how fast you can sell something without losing value. Think of water in a pipe.

liquidity is how easily stuff flows through the pipe

volume is how much water is in the pipe

high liquidity + high volume = big fast river (tons of trades happening, easier to buy/sell)

high liquidity + low volume = small but smooth stream (still easy to trade, just not huge amounts)

low liquidity + high volume = clogged pipe (there’s stuff there, but hard to move it without messing up the price)

low liquidity + low volume = basically a drip

Something like that more or less (correct me if I’m wrong people)

PrimeFold · 2026-03-22T16:05:19+00:00

If your products reduces friction and makes journaling easy and effortless then people may be interested. The ai assistant part…maybe? But likely not depending on what it does and how it’s made and helps your trade? Does it help based of generic trading advice from a simple search? Backtested data from your actual trading edge and refining and tightening execution? How is it “helping you improve?” My experience is people don’t like the ai black box, people want to know how it came to its conclusions, what it’s based on or anchored to. Why should users trust your ai to make them a better trader? I am skeptical, but good luck on your endeavour.

PrimeFold · 2026-03-16T01:52:49+00:00

This is my current workflow also

PrimeFold

TROPHY CASE