CALI, COLOMBIA

Most companies introduce new products with presentations and laboratory demonstrations. Gang Wayz Bags took a different approach.

To demonstrate confidence in the company’s patented bulletproof fashion bag, CEO Raphael Ranger stood behind his invention during two live ballistic demonstrations filmed in Cali, Colombia.

The first test was conducted in a controlled environment to confirm the performance of the bulletproof bag under live-fire conditions.

“Even though I knew the product would perform as designed, it’s human nature to feel stressed when you’re standing behind it,” Ranger said. “That first test gave us the confidence to move forward.”

The second demonstration raised the difficulty significantly. The team carefully coordinated a realistic attack scenario after many viewers questioned whether the ballistic sling bag would actually be useful in a real-life emergency. Rather than simply shooting at a stationary target, the objective was to evaluate how the deployable ballistic shield behaved during a more dynamic situation.

One of the biggest questions from viewers was whether the shield would be pushed backward by bullet impacts. According to Gang Wayz , the demonstration showed that the ballistic panel remained remarkably stable after being struck, with very little movement as the impact energy was distributed throughout the panel.

The team also evaluated what happens if a projectile strikes the area where the user’s hand is positioned while holding the shield. Because only the knuckles are in contact with the handles, Ranger reported experiencing no injuries during the demonstration.

The patented bulletproof crossbody instantly deploys into a substantially larger ballistic shield within 0.5 seconds. Designed as a premium armored fashion bag, it combines everyday style with rapid ballistic protection while remaining practical for everyday carry.

“We wanted to answer the questions people were asking instead of asking them to simply trust us,” Ranger said. “People wanted to know whether the shield would move after being hit, how it would behave during a realistic attack, and what would happen if a round struck near the hand. We decided to demonstrate it.”

Gang Wayz says the demonstrations represent 2 years of research and development focused on creating a product that can be carried every day while providing deployable ballistic protection when needed.

The company emphasizes that no protective product can eliminate the dangers associated with firearms and that every live-fire demonstration involves significant planning, strict safety measures, and inherent risk.

The videos have generated widespread discussion online, with viewers debating the results and praising the CEO’s willingness to personally stand behind his invention. For Gang Wayz Bags, the demonstrations were intended to show how the product is designed to perform under ballistic impacts and to answer questions raised by the public through real-world testing.

CEO Raphael Ranger during the first test.

The bulletproof bag was shot 8 times with no injury to Raphael.

2nd test showing a real life simulation with 6 bullets.

Media Contact:
For media inquiries about this release, submit an inquiry.
https://send.heraldengine.com/contact/82268/fe7c0c2c652a3a919ecd1f33c6a58d549422b30b498fa736dd3bead589c3573b

NEW YORK, USA

I Gave One $75,000 for 21 Days to Find Out

Every trader has faced that agonizing moment. It is 3:00 AM, your eyes are bloodshot, you are staring at a cluster of technical indicators on a 15-minute chart, and your gut is waging war against your risk management strategy. You know emotional trading is financial suicide. Yet, as humans, we are wired to panic at the dips and get intoxicated by the rallies.

For years, Wall Street’s elite quantitative funds have used proprietary algorithms to exploit human emotion, executing thousands of trades a second to capture market alpha. But the average retail investor has been left with dumbed-down trading bots—simple rule-based scripts that get wiped out the moment market volatility shifts.

Then, generative AI evolved into agentic AI.

Instead of just predicting the next word in a sentence, modern AI agents can reason, execute multi-step workflows, analyze macroeconomic sentiment in real-time, and execute trades autonomously without human intervention.

To test whether this new frontier of artificial intelligence could actually generate consistent alpha, I did something equal parts thrill-seeking and scientific: I made a crypto deposit of $75,000 to my trading account on an autonomous AI trading agent for 21 days. I chose Stablecoin to avoid sudden fluctuations.

No manual overrides. Just $75k, 21 trading days, and an AI agent calling the shots on a secure live trading environment.

Here is what happened, the exact performance data, and what this experiment reveals about the future of AI-driven investing.

What Is Agentic AI in Trading? (And Why Simple Bots Fail)

Before diving into the $75,000 trade log, we need to address a critical distinction that most retail traders miss: the difference between a legacy trading bot and an agentic AI trading platform.

Traditional algorithmic trading relies on hardcoded logic. If parameter $A$ occurs, execute trade $B$. The moment the market shifts from a trending environment to a range-bound environment—or when an unexpected Federal Reserve announcement hits the wires—these rigid bots fall apart.

Enter GigaromAI: The Autonomous Trading Engine

To run this experiment, I needed an architecture capable of genuine reasoning and adaptive execution. I chose GigaromAI, an advanced platform designed to deploy autonomous AI agents for financial analysis and automated portfolio management.

Unlike standard trading platforms, GigaromAI leverages an agentic architecture. It doesn’t rely on a single static model; instead, it orchestrates specialized AI agents working in consensus:

* The Macro & Sentiment Agent: Continuously scans global financial news, SEC filings, earnings call transcripts, and market sentiment.

* The Quantitative Analysis Agent: Calculates technical indicators, market liquidity, order book depth, and probability distributions.

* The Risk Management Agent: Serves as the internal check-and-balance, enforcing strict stop-loss protocols, position-sizing rules, and maximum drawdown limits.

By processing thousands of data points simultaneously, GigaromAI formulates hypothesis-driven trades, cross-examines them internally across its agent network, and executes them in milliseconds—all while adapting to changing market conditions in real time.

The Setup: Protocol, Parameters, and Risk Rules

Giving an AI $75,000 of real capital requires strict guardrails. I wasn’t looking to create a high-stakes gambling machine; I wanted to test if GigaromAI could generate superior risk-adjusted returns (a higher Sharpe ratio) compared to a passive S&P 500 index fund ($SPY).

The Rules of the Experiment

1. Starting Capital: $75,000 USD (Stablecoin).

2. Duration: 90 Trading Days.

3. Benchmark: SPDR S&P 500 ETF Trust ($SPY).

4. Intervention: Zero manual overrides allowed (unless system error occurred).

5. Asset Class Universe: US Equities (Large-Cap & Mid-Cap), Tech ETFs, and select liquid instruments.

6. Risk Constraints:

    * Maximum risk per trade: $2%$ of total portfolio value.

    * Hard daily stop-loss limit: $3.5%$.

    * Dynamic trailing stop-loss activated at $+4%$ profit targets.

With my trading plan and the agentic machine activated on my GigaromAI elite founder subscription, I pressed start.

The 21-Day Trade Log: Week-by-Week Breakdown

Week 1: The Cold Start & The Earnings Season Trap (Days 1–7)

* Starting Balance: $75,000

* Week 1 Ending Balance: $77,850

* Net Return: $+3.8%$

* S&P 500 Return: $+1.2%$

The first week were agonizingly quiet. While I expected the AI to immediately open high-frequency trades, GigaromAI’s Risk Management Agent kept $60%$ of the account in cash.

It was scanning for asymmetric risk-reward setups.

Its first major move occurred during a turbulent tech earnings week. While retail sentiment on X (formerly Twitter) was wildly bullish on major semiconductor stocks ahead of earnings, the Sentiment Agent detected an underlying divergence: insider selling combined with rising option implied volatility skew.

Instead of buying the hype, GigaromAI initiated a delta-neutral hedge position, longing low-valuation cloud infrastructure plays while shorting overextended hardware stocks.

When earnings disappointed and tech equities pulled back, the strategy paid off handsomely. By the end of Week 1, the portfolio was up $+3.8%$, outperforming the benchmark while taking significantly less directional risk.

Key takeaway from Week 1: An AI agent’s greatest asset isn’t just knowing when to trade—it’s knowing when to sit on cash and preserve capital.

Week 2: Navigating the Macro Shockwave (Days 8–14)

* Starting Balance: $77,850

* Week 2 Ending Balance: $82,620

* Net Return (Cumulative): $+10.16%$

* S&P 500 Return (Cumulative): $+2.8%$

Week 2 provided the ultimate stress test. Mid-month, unexpected inflation data sent shockwaves through the market. The S&P 500 experienced a sharp 2.4% sell-off in a single trading session.

This is where human traders fail. Fear takes over, leading to panic selling at the absolute bottom or revenge trading to recover losses.

GigaromAI didn’t panic. Within seconds of the economic data drop, its Macro Agent processed the inflation reports, re-calculated portfolio variance, and executed three distinct moves:

1. Triggered tight trailing stops on vulnerable growth positions, locking in profits.

2. Rotated $25%$ of capital into defensive value sectors and interest-rate-resilient equities.

3. Initiated algorithmic scale-in orders on oversold quality tech stocks as market panics peaked.

While human traders were liquidating positions at the low, GigaromAI was systematically buying the dip based on statistical mean reversion probabilities. By the time the market rebounded the following week, the account experienced its largest equity curve breakout of the entire experiment.

Week 3: Profit Realization and High-Volatility Alpha (Days 15–21)

* Starting Balance: $82,620

* Final Balance: $88,425

* Total 21-Day Return: $+17.9%$

* S&P 500 90-Day Return: $+4.6%$

By the final week, the performance difference was stark. While passive index investors achieved a respectable $4.6%$ over the 21-day window, GigaromAI’s active, multi-agent management yielded a total return of $+17.9%$—outperforming the benchmark index by more than $3x$.

More importantly, the total maximum drawdown across the entire 21 days was just $2.1%$, compared to the benchmark’s maximum drawdown of $4.8%$.

Deep-Dive Analysis: The Performance Metrics

To truly answer whether an AI agent can beat the market, simple total returns aren’t enough. We must evaluate risk-adjusted metrics to ensure the excess performance wasn’t simply the result of taking on excessive leverage or hidden risk.

Performance Summary Table

3 Critical Lessons Learned from Letting AI Manage $75,000

1. Emotionless Execution Beats Human intuition 10 Out of 10 Times

The biggest source of loss for retail traders isn’t bad stock selection—it’s cognitive bias. We hold losers too long hoping they will break even, and sell winners too early out of fear of losing profits.

GigaromAI exhibited zero emotional attachment. If a trade setup invalidated its initial thesis by even a fraction of a percent, the position was closed instantly. No hope. No copium. Just execution.

2. Multi-Agent Consensus Prevents Hallucinations

A common critique of using Large Language Models (LLMs) for finance is hallucination—making decisions based on false patterns or incorrect data.

GigaromAI overcomes this through multi-agent validation. The execution agent cannot open a trade unless the risk agent approves the exposure parameters and the sentiment agent confirms macroeconomic alignment. This cross-verification loop kept false trade signals near zero.

3. Alpha Is Moving to the Micro-Moments

The modern market moves too fast for human analysis. By the time a news event appears on financial news television, the market has already priced it in. Agentic AI platforms level the playing field by processing real-time web data, order flow imbalance, and sentiment shifts in milliseconds.

How to Get Started with Agentic AI Trading

If you want to move away from emotional trading and explore autonomous AI portfolio management, here is the roadmap to get started safely:

1. Understand the Architecture: Educate yourself on how agentic workflows differ from simple rule-based bots. Explore platforms like GigaromAI to see how autonomous agent workflows function in live financial environments.
2. Define Strict Risk Constraints: Your AI Agent sets maximum drawdown limits, position sizing limits, and daily loss limits before enabling live trading capabilities.
3. Monitor, Don’t Micro-Manage: The purpose of an AI agent is to eliminate human bias. Once your risk protocols are programmed, let the AI execute without manual interference unless a fundamental parameter breaks.

The Verdict: Can AI Beat the Market?

Can an AI trading agent actually beat the market?

Based on this 21-day experiment, the answer is a resounding yes—if you are using a true agentic AI platform rather than a simple script.

Turning $75,000 into $88,425 in 21 days while maintaining lower drawdown risk than the broad market proved that autonomous financial AI is no longer a future concept. It is here today.

Platforms like GigaromAI are democratizing institutional-grade quantitative tools for everyday investors, replacing emotional human guesswork with systematic, data-driven execution.

The financial landscape has changed forever. The only question left is: Will you continue trading with human intuition, or will you let AI give you the quantitative edge?

Visit for more information : www.gigarom.com

Media Contact:
For media inquiries about this release, submit an inquiry.
https://send.heraldengine.com/contact/82413/1fa058c948da96ac831ee57a3e721928d149a7729b65f7a1c8c64c069e366d21

HOLLYWOOD, FL

Torah education nonprofit maintains Candid’s highest transparency rating and reports its 2025 program outcomes

BeEzrat HaShem Inc., a Florida-based 501(c)(3) Torah education and outreach organization, has received Candid’s 2026 Platinum Seal of Transparency for the third consecutive year.

The Platinum Seal is the highest of Candid’s four transparency designations. According to the organization, fewer than 1% of the approximately 1.8 million U.S. nonprofits listed in Candid’s database have achieved the Platinum level.

“Earning Platinum once shows that an organization can prepare a strong report. Earning it three years in a row demonstrates that transparency is part of how we operate,” said Rabbi Yaron Reuven, founder and president of BeEzrat HaShem Inc. “Our donors support us because they trust us to spread Torah, perform chesed, and help feed people in need. That trust is sacred, and the Platinum Seal allows us to demonstrate through publicly reported data how donations are being used.”

Candid’s Platinum designation requires participating nonprofits to disclose measurable program outcomes in addition to information about their finances, governance, mission, and operations. Organizations must review and update their profiles annually to maintain the designation.

3 Years Platinum · Top 1% U.S. Nonprofits

Candid has also reported that nonprofits displaying a Seal of Transparency receive, on average, 62% more donor contributions than organizations without a seal.

2025 Program Results Reported to Candid

BeEzrat HaShem Inc. reported the following results for 2025:

• Raised $2.3 million, primarily through individual donors who discovered the organization through its online Torah education programs rather than through foundation grants.
• Provided food or financial assistance to approximately 238,000 Jewish individuals.
• Produced and distributed more than 15,000 Torah lectures and over 500 Torah-based films.
• Operated 23 YouTube channels in 15 languages, reaching more than 200,000 combined subscribers and followers.
• Distributed more than 280,000 complimentary copies of 36 sefarim authored by co-founder Rabbi Efraim Kachlon, compared with 150,000 copies distributed in 2024.
• Distributed more than 2.3 million outreach materials, including CDs, USB drives, and kiruv cards, at no charge.
• Supported religious scholarship infrastructure consisting of two kollels for Dayanut, one Beit Din, and 12 rabbis and speakers.
• Developed and operated two proprietary platforms: the free BH Torah lecture app at www.BeEzratHaShem.org/getapp and the AI Rabbi question-and-answer platform at www.AIRabbi.org.
• Launched the Teshuva Music and BH Kids YouTube channels, featuring original kosher educational and entertainment content.

“Many nonprofits of our size do not pursue the Platinum designation because of the level of documentation required,” Rabbi Reuven said. “We continue to pursue it because our supporters deserve clear, measurable information showing that we are responsible stewards of their donations.”

BeEzrat HaShem Inc. uses individual donations to support Torah education, religious book publishing, digital educational resources, and chesed programs.

Additional information about the organization and its programs is available at www.BeEzratHaShem.org. Donations can be made at www.BeEzratHaShem.org/donate. The organization’s verified Candid Nonprofit Profile is also available through Candid.

About BeEzrat HaShem Inc.

BeEzrat HaShem Inc. is a Florida-based 501(c)(3) nonprofit organization focused on Torah education, outreach, publishing, and chesed initiatives. The organization operates 23 YouTube channels in 15 languages, two proprietary digital applications, multiple educational websites, and a global complimentary book-distribution program. BeEzrat HaShem Inc. was founded by Rabbi Yaron Reuven and co-founded by Rabbi Efraim Kachlon. Its activities are funded primarily by individual donors who engage with the organization’s online Torah education programs.

Media Contact:
For media inquiries about this release, submit an inquiry.
https://send.heraldengine.com/contact/81651/655199f664100885a18f3c32497a2229260d86e9910386cb6e8add58b1a6f4c1