Mason Reid

Living somewhere between ai agents, llms, quantum stuff, prediction markets and crypto

[Mason Reid @zer0ixm]

https://t.co/hxNGmQqTip

[codila @0xCodila]

Jev is "Internet" moment for AI: up to 193x faster and 444x cheaper in tests with Claude Fable 5.1 and GPT-6 Astra

Whar is Jev, how to use and unlock its real 100х advantage in my 10-page research:

step 1 → meet Jev: LLM writes, agents act, Jev chooses the next move - split intelligence from execution

step 2 → turn every agent fork into three primitives: Choice selects one route, Score measures a defined scale, Noul returns the probability of yes

step 3 → build before getting access: use TypeSafe’s official adapter with OpenAI, Anthropic or xAI, then swap in Jev without rebuilding the graph

step 4 → setup first Jev: one state, three parallel decisions, risk-based thresholds and a real queue your agents can execute

step 5 → batch decisions instead of serializing them: 13 questions in one call ran 10x faster and 12.2x cheaper than 13 sequential calls

step 6 → place Jev at every bounded fork: choose the agent, model, tool, browser action or human escalation, then read fresh state

step 7 → benchmark the entire loop: Browser Use hit Google Flights in 7.1s, Every ran 777 checks in under 0.7s, Mobile Jev completed 9 actions in 21s

step 8 → rank wide, read narrow: Jev cut wrong Hermes skill loads from 16.8% to 7.3% and pushed legal Top-10 retrieval from 38% to 62%

step 9 → steal a system, not a prompt: Chief of Staff, model router, inbox firewall, research feed, browser controller and safety gate all use State → Questions → Action → Verify

step 10 → keep Jev out of math, writing and irreversible execution: code computes, LLMs create, Jev decides, fresh state proves the result

the result: one slow, expensive agent becomes an always-on decision machine that routes, scores and escalates in milliseconds

Copy the complete 10-page Jev blueprint - then read full 10-step roadmap below ↓

[Noisy @noisyb0y1]

She's 18 built an AI agent with Opus 5 and sold it to Anthropic for $3.2M - and came to Stanford to show how to do it from scratch:

00:34 - how Opus 5 builds a $3.2M agent in one evening
15:34 - 4 agents replaced 400 Anthropic engineers
34:47 - from first prompt to a $3.2M check from Anthropic

after watching I spent 60 minutes building my first agent - it cut my workday by 90% and a week later I got a $100k check from Anthropic:

save & watch - article below on how to go from one prompt in Claude Code to an agent people pay millions for.

[Zakir Hossain @zakiraicoder]

19 year old Japanese student built a trading bot with Claude Code in 2 days.

Used his iPad as a second monitor.

First night: $6,732 profit.
Starting capital: $68.

Total profit so far: $750,000.

[ 𝐍𝐨𝐭𝐞: 𝐅𝐨𝐥𝐥𝐨𝐰 𝐌𝐞 @zakiraicoder 𝐅𝐨𝐫 𝐢𝐧𝐬𝐭𝐚𝐧𝐭𝐥𝐲 𝐚𝐮𝐭𝐨 𝐃𝐌]

Here's how it works👇

The bot scans over 50 markets simultaneously.

Syncs live BTC data from Binance every second.

Spots price errors before humans even notice.

The edge is pure speed + pattern recognition.

While traders stare at charts trying to predict the next move, his bot is already executing on mispricing across dozens of markets.

No guessing.
No emotions.
No hesitation.

Just Claude Code logic finding gaps that close in seconds.

He built the entire system in 48 hours:

→ Claude Code handles the trading logic
→ Binance API feeds real-time BTC data
→ iPad displays multi-market monitoring
→ Executes trades when arbitrage windows open

The system runs 24/7.

Every price dislocation = profit opportunity.

Most people are still trading manually, refreshing charts, second-guessing entries.

Meanwhile this 19 year old engineering student turned $68 into $750K by letting Claude Code do what humans can't: process 50 markets instantly and execute without fear.

Why are people still trading manually?

I'm giving away the exact Claude Code setup for free.

24 hours only.

To get it:
1️⃣ Comment " Claude "
2️⃣ Like and Repost
3️⃣ Follow @zakiraicoder so I can send it via DM.

I'll DM you the complete setup.

[Jarrod Watts @jarrodwatts]

I built a trading bot with Jev!

Jev decides if it should "buy" or "sell", given the price feed of an asset pair, and executes real trades.

It uses Monad to place the orders on Kuru's on-chain order book in every 300ms block.

Demo link → https://t.co/vwl2SUu4jm

[beamnxw ./ @beamnxw]

this is pure f*cking treasure

15 GitHub projects with 1.21M combined stars that can form a real agent stack

specs. memory. web data. documents. context. sandboxes. monitoring. video

01 hermes-agent
▸ https://t.co/fpJlyU9RoS

02 OpenSpec
▸ https://t.co/2nB3z9aICv

03 caveman
▸ https://t.co/w7TLtPTg4o

04 Scrapling
▸ https://t.co/uwfvY3AtBi

05 Docling
▸ https://t.co/ETkC2ak6as

06 PageIndex
▸ https://t.co/RcH9jlUKfl

07 mem0
▸ https://t.co/Oiqdog6LXF

08 headroom
▸ https://t.co/Lw1ouzUoE3

09 Daytona
▸ https://t.co/1e0hLQpaRs

10 TrendRadar
▸ https://t.co/aIrqtOtPn8

11 Fabric
▸ https://t.co/AoCw2SjDFk

12 spec-kit
▸ https://t.co/Lztf6n09fW

13 hyperframes
▸ https://t.co/EEk3joz0GZ

14 OpenMontage
▸ https://t.co/D5ebisVk0d

15 AI Engineering Hub
▸ https://t.co/aEXjUByMxO

the loop:

define the job → collect the evidence → parse the docs → save the memory → compress the context → run the code safely → watch what changes → ship the output

the entire stack is open source

save this to build your own business with the help of an AI employee ⭣

[rari @0xwhrrari]

Stanford AI engineering course:

"Anyone can build an AI agent in 60 minutes"

Prompt → Agent → Automation → Revenue

Stanford just released a course on building AI agents from scratch

00:00 - Build your first AI agent
48:17 - Create agents without coding
54:39 - Make $100K+ per month with agents

While you scroll, someone else is learning Anthropic's $750,000 skill

This free course is better than most paid AI agent courses

Bookmark and watch it today

Then read the article below

[Liam @liam_fallen]

I made a Grok Bot that makes you invisible online.

It hunts down your personal info and removes it.

→ Finds where it’s exposed
→ Submits removal requests
→ Confirms your data was removed
→ Escalates requests that were ignored

I built it for myself.

You can use it too.

Link below 👇

[TechHalla @techhalla]

try this at home

but only if you use these GPT Image 2.5 and Seedance 2.5 prompts below 👇

[Mason Reid @zer0ixm]

@0xCodila @poteto this is the kind of prompt list that’s actually useful 👀

not “make me more productive” �� real roles, real handoffs, real constraints. the Chief of Staff + specialist bots setup is probably the part I’d steal first.

[Mason Reid @zer0ixm]

@ai_explorer25 @karpathy @bcherny @trq212 this is the kind of follow list I actually want 👀

less “AI influencer” noise, more people actually building the models, tools and workflows everyone else talks about.

[Mason Reid @zer0ixm]

@m_adams this is insanely ambitious 👀

mapping government power is one thing — letting AI continuously model and monitor it is where the transparency vs. surveillance line gets very interesting.

[Mason Reid @zer0ixm]

@Vikram_AI_ this is the kind of “secret” that sounds obvious only after someone shows you the math.

no magic picks, no intuition — just probability, payout, and discipline.
honestly, the fact he explained the whole edge in a 45-minute lecture is wild.

[Ajit kumar @ajitcodes]

SpaceXAI engineer, Lauren Tan:

"99% of people using GrokBot just for 1% of its real power. They run 1 agent without "loop" & "graph"

I'm running a team of 20+ GrokBot agents, fully autonomous. I have a Chief of Staff agent, a PM agent and 20+ workers - that's the new stack of engineer"

In a 1-hour session, a SpaceXAI engineer showed how to build a team of effective AI agents from scratch

this is worth more than a $500 agentic engineering course

watch this workshop today, then read how to build a fleet of GrokBot agents in the article below

[Mason Reid @zer0ixm]

Claude can now help you do Wall Street-style stock research for free.

A lot of the work people associate with highly paid equity analysts — market research, earnings breakdowns, valuation, risk analysis, sentiment, and portfolio review — can now be done with the right context and prompts.

Here are 10 prompts worth saving ↓

1. Market Analysis

Focus on [sector or stock] and analyze the current trends affecting it.Identify emerging patterns, catalysts, risks, and potential opportunities. Include the latest earnings reports, balance-sheet data, industry news, and relevant macro developments.Finish with the 3–5 factors that could matter most over the next 6–12 months.

2. Portfolio Diversification

Review this portfolio: [insert current sectors/stocks/weights].Identify concentration risks and suggest ways to create a more balanced portfolio. Explain which sectors or asset exposures are missing and provide examples of securities that could improve diversification.Explain the reasoning behind each suggestion and the risks it introduces.

3. Risk Management

Analyze the risk-management framework for this strategy: [insert strategy/positions].Explain how stop-loss rules, position sizing, diversification, maximum drawdown limits, and portfolio exposure could be structured.Use concrete examples and show how the portfolio would respond under several adverse market scenarios.

4. Technical Analysis

Perform a technical analysis of [stock/ticker].Review recent price action, volume, trend structure, support/resistance, moving averages, RSI, and other relevant indicators.Summarize the bullish and bearish signals and explain what conditions would strengthen or invalidate each scenario.

5. Economic Indicators

Explain how GDP growth, unemployment, inflation, interest rates, and other major economic indicators could affect [sector or stock].Show how investors can interpret changes in these indicators when evaluating the company or sector.Include both positive and negative macro scenarios.

6. Value Investing

Explain the core principles of value investing and how investors identify potentially undervalued companies.Analyze [insert companies/stocks] using valuation metrics, earnings quality, cash flow, balance-sheet strength, competitive position, and potential catalysts.Explain what would make each company genuinely undervalued versus simply cheap for a reason.

7. Market Sentiment

Analyze current market sentiment around [stock or sector].Consider analyst commentary, news flow, investor positioning, options activity, social sentiment, and other useful indicators.Explain how sentiment differs from fundamentals and how the two could be combined in a trading or investment framework.

8. Earnings Report Analysis

Analyze the latest earnings report for [company].Focus on revenue growth, margins, EPS, free cash flow, guidance, segment performance, debt, capex, and management commentary.Highlight what beat or missed expectations and identify the metrics most likely to influence the stock going forward.

9. Growth vs. Dividend Stocks

Compare growth stocks with dividend stocks using [insert examples].Explain the return drivers, valuation risks, volatility, cash-flow characteristics, and sensitivity to interest rates for each style.Describe the market environments in which each approach has historically tended to perform differently.

10. Global Events

Analyze how major global events — geopolitical conflicts, supply-chain shocks, pandemics, elections, or financial crises — could affect [sector or stock].Map the first-order and second-order effects on revenue, costs, demand, valuation, and investor sentiment.Then outline several risk-management scenarios an investor could consider.

The real upgrade isn’t asking Claude:

“Should I buy this stock?”

It’s using AI to build the research process around the decision:

Market → Fundamentals → Valuation → Risk → Sentiment → Scenarios → Thesis

That’s a much better way to use it.

Save these 10 prompts before they disappear into the timeline. 📌

[Mason Reid @zer0ixm]

https://t.co/sgJ913L1Sn

[Mason Reid @zer0ixm]

Andrej Karpathy just turned years of work at OpenAI and Tesla into a free 2-hour masterclass on how modern AI systems are actually built.

The progression is the part worth paying attention to:

Prompts → Harnesses → Loops → Graphs → Self-Improving Systems

This isn’t another lesson on writing prettier prompts.

It’s about the layer underneath: how you wrap models with tools, memory, evaluation, control flow, and feedback so the system can keep executing instead of waiting for another message from you.

A lot of people spend thousands on AI engineering bootcamps and still never get this mental model.

Karpathy explains it in two hours for free.

You probably won’t watch the whole thing right now.

Save it anyway. Don’t let it disappear into the timeline.

Watch the lecture first, then use the step-by-step guide below to build your first real loop 👇👇

[Mason Reid @zer0ixm]

https://t.co/hxNGmQqTip

[Mason Reid @zer0ixm]

@RohOnChain this is the part I’d want to stress-test before getting excited 👀

101 strategies sounds great, but the real edge is how many survive out-of-sample validation, costs, slippage and regime changes.

generation is cheap now. rejection is the valuable part.

[Mason Reid @zer0ixm]

@0xCodila this is the real unlock 👀

the moment your agent starts building and managing other agents, you stop scaling prompts and start scaling systems.

that’s a completely different level of leverage.

[Mason Reid @zer0ixm]

@AIGryffindor yeah, that’s exactly the shift 👀

I probably wouldn’t kill the blog entirely yet — humans still need a place to discover and trust the source. but I’d absolutely start designing the content so agents can consume it first-class instead of treating MCP like an add-on.

[Mason Reid @zer0ixm]

Something weird is happening to web research.

Mach33 quietly added an MCP connector 20 days ago - and agent usage has already overtaken normal research-page visits.

The difference in behavior is even crazier:

A typical website user reads 2 analyses.

A typical MCP user makes 36 research calls.

That’s roughly 18× deeper engagement.

This is the shift people are missing:

humans browse research.
agents consume it continuously.
Once your AI can pull the right research directly into its workflow, the website stops being the interface.

The agent becomes the interface.

And this trend is apparently accelerating across both free and paid users.

If you use Grok, Claude or ChatGPT for space / SpaceX research, connecting the research layer directly might be the next obvious upgrade. 👀

https://t.co/qWTK0txxMG

[Aaron Burnett @aaronburnett]

If you're a fan of Mach33 research and you're not plugging the MCP/Connector into your agents (my preferred is grok bot) then you are falling behind.

just a few moments ago, 20 days after a very quiet release, total research interactions from MCP eclipsed total research visits on our site over the same time.

The median user on the site has read 2 analysis over the last 20 days and the median user on the MCP has made 36 analysis calls. 18x more depth of engagement.

the really nutty thing is that this trend is actually meaningfully accelerating over the same amount of time, across free users and paid users.

this does not include internal users which would dramatically accelerate this trend.

do yourself a favor and connect the Mach33 MCP to grok/claude/chat gpt whatever you use.
https://t.co/HFjgjPWmds

You're agent will get much smarter on any space/spacex related question.

[Elon Musk @elonmusk]

We will work on this

[Mason Reid @zer0ixm]

@Rossst_03 the funny part is the “$500k AI skill” still comes back to linear algebra on a whiteboard 🤯new models, new agents, new tooling — same old math underneath everything.