u/Private_Tank

**** this gamemode. I do everything in the game to keep up even tho I dont really want to do it 100%. But this time consuming piece of **** will get me out of the game. Holy **** I hate it

I’m designing a system where many users can create their own independent search agents.
Each agent has its own search criteria and runs periodically to retrieve fresh results.
The challenge is that every user can have multiple agents, and each agent can perform multiple different searches. As the number of users grows, the number of individual searches grows very quickly.
I need the searches to remain independent and reasonably real-time, while also preventing the system from becoming overloaded as the number of users and agents increases.
How would you architect the scheduling and execution layer for this kind of system so that it can scale to a very large number of users and recurring searches?
I’m particularly interested in how you would approach scheduling, concurrency, backpressure, and prioritization without letting a traditional queue grow indefinitely.

Im just curious for the reason why you would choose this or that

Hallo zusammen,

ich beschäftige mich aktuell mit meinem ETF-Setup und würde gerne eure Einschätzung hören.
Ich habe momentan noch relativ wenig investiert, mein Depot liegt bei unter 10.000 € und aktuell steckt alles ausschließlich im MSCI World. Andere Anlagen wie Emerging Markets, Einzelaktien, Anleihen oder ähnliches habe ich nicht.

Jetzt überlege ich, ob der FTSE All-World für mich langfristig die bessere Wahl wäre, weil dort zusätzlich Schwellenländer enthalten sind und ich damit eine breitere Abdeckung hätte.

Meine Fragen:

Würdet ihr bei dieser Depotgröße noch wechseln oder einfach den MSCI World weiter besparen?
Macht der zusätzliche Emerging-Markets-Anteil im FTSE All-World langfristig einen relevanten Unterschied?

Würdet ihr einen Wechsel jetzt machen, solange das Depot noch relativ klein ist, oder gibt es keinen wirklichen Grund dafür?

Gibt es aus eurer Sicht Nachteile beim FTSE All-World gegenüber dem MSCI World?
Mir geht es nicht darum, den perfekten ETF zu finden, sondern eine einfache langfristige Lösung für die nächsten Jahrzehnte aufzubauen.

Danke für eure Einschätzungen.

Hey everyone,
I'm working on a side project that monitors marketplace listings in real time based on user search queries. Right now, the flow is pretty straightforward: my scraper picks up new listings (title, price, description, and the listing URL) and passes them to a cheap LLM using a structured system prompt to check two things:

1. Relevance: Is it actually the product the user is looking for (and not just an accessory, a wanted ad, or a completely different model)?

2. Deal quality: Is the price significantly below average market value?
   Users can set up alerts for literally anything (camera gear, power tools, vintage stuff, etc.), so I can't just hardcode a fixed category tree.

Currently, I'm passing trimmed listing data (or sometimes just feeding the raw listing URL and snippet) in batches to a fast model like Gemini Flash, while caching seen items in Redis.
This works, but as the feed volume scales, sending every new item to an LLM is going to get expensive fast.

I'm curious if anyone here has built something similar or has ideas on how to optimize this:

Zero-LLM classification: Since search queries are totally arbitrary, is there a good way to filter out irrelevant items/accessories using something like embeddings or lightweight NLP before calling an API?

Pricing logic without LLM: Any neat approaches to dynamically figure out price ranges for arbitrary items programmatically?

Token saving tricks: Is URL-only parsing or passing small raw snippets better in practice? What multi-step pipelines have worked well for you to drop 90%+ of API calls?
Would love to hear how you'd tackle this!

Hey everyone,
I'm working on a side project that monitors marketplace listings in real time based on user search queries. Right now, the flow is pretty straightforward: my scraper picks up new listings (title, price, description, and the listing URL) and passes them to a cheap LLM using a structured system prompt to check two things:

1. Relevance: Is it actually the product the user is looking for (and not just an accessory, a wanted ad, or a completely different model)?

2. Deal quality: Is the price significantly below average market value?
   Users can set up alerts for literally anything (camera gear, power tools, vintage stuff, etc.), so I can't just hardcode a fixed category tree.
   Currently, I'm passing trimmed listing data (or sometimes just feeding the raw listing URL and snippet) in batches to a fast model like Gemini Flash, while caching seen items in Redis.

This works, but as the feed volume scales, sending every new item to an LLM is going to get expensive fast.
I'm curious if anyone here has built something similar or has ideas on how to optimize this:
Zero-LLM classification: Since search queries are totally arbitrary, is there a good way to filter out irrelevant items/accessories using something like embeddings or lightweight NLP before calling an API?

Pricing logic without LLM: Any neat approaches to dynamically figure out price ranges for arbitrary items programmatically?
Token saving tricks: Is URL-only parsing or passing small raw snippets better in practice? What multi-step pipelines have worked well for you to drop 90%+ of API calls?

Would love to hear how you'd tackle this!

What if someone brings a block debuffs champ alongside mercurial? This isnt even rare and will easily counter the counter?

+ bringing a Provoker seems more of an Investment than bringing a block debuff Support.

I love the increase in Variety but I cant see a big mercurial nerf here

I swear last 3 rotations I was able to place the heal reduction when the eclipse counter hit 2 and everything worked out fine.

Now I place it and somehow he still turns the heal reduction to a 3 turn continuous heal

The more I get into trading, the less I can believe it

Is Hermes capable of Controlling multiple instances of LDPlayer with the proper mcp?

I’ve been trading the CRT setup for a while now and finally feel confident with it.
My rules are pretty simple:
- Im looking for trades on the 1h or 15 mnute Chart
- Only trade in the direction of the higher timeframe trend.
- Risk:Reward is fixed at 1:1.5.
- Take profit is set around the end of Candle 1 of 3.
- I focus on high-probability setups only.
The result is actually pretty good. My win rate is high enough that I’m consistently profitable before fees.
The problem is that my exchange charges 0.06% per side, and when I calculate my results, the fees often eat more than 50% of the profit from a winning trade.
At that point it feels like I’m doing all the hard work just for the exchange.
Am I making a mistake in how I’m trading CRT?
Should I be aiming for larger moves, a higher RR, holding longer, trading higher timeframes, or is this simply the reality of trading small intraday moves?
I’m curious what more experienced traders would do in this situation.

Im new to this and its hard for me to extract the key information. I will keep watching the videos, but can someone summarize the needed signals in order to go in on a trade?

Also what Symbols are you trading?

WHO:
I was contacted by/seen an advertisement for residential-testers.com. I do not know the people behind the website personally. The company claims to provide a testing service using devices installed at users' homes.

WHAT:
I signed up on residential-testers.com using my real information. They sent me a Raspberry Pi 5 (4GB RAM) with a passive cooling case and a 32GB SD card.

The idea seems to be that I connect the Raspberry Pi to my home network and they pay me monthly for allowing them to use my residential internet connection.

I have not connected the device yet because I am concerned about security risks and whether this could be used for illegal activity.

WHEN:
I saw the advertisement on Reddit recently and signed up shortly afterwards. The device arrived after registration.

WHERE:
This happened online. Website:
residential-testers.com

WHY:
I want to know whether this is a legitimate market research/testing service or if this is a scam or a risky service that could abuse my residential IP address.

HOW:
I have not paid them any money. They sent the hardware to me. They appear to make money by using residential internet connections, but I do not fully understand what traffic will go through the device or what they are doing with my connection.

Questions:
- Has anyone heard of residential-testers.com?
- Is this a known company?
- Could this put my IP address or home network at risk?
- Is there a safe way to test this device (for example using a separate VLAN)?

I am currently working on an MQL5 Expert Advisor and have hit a technical hurdle regarding data processing. My objective is to perform all internal calculations—specifically for stop-loss, take-profit, and entry triggers—based on synthetic Heikin Ashi values rather than raw OHLC market data.

In my manual analysis, I am using Heikin Ashi candles to smooth out market noise, and I would like my EA to follow the exact same logic.

My main questions:

• What is the most robust way in MQL5 to transform the standard price stream into a synthetic Heikin Ashi stream so that all subsequent indicators and logic gates use these values as the primary source?
• How can I ensure that when the EA calculates a swing-high or swing-low (e.g., for setting a stop-loss), it is strictly looking at the calculated Heikin Ashi high/low values rather than the raw tick-based high/low?
• Is there a recommended approach to ensure that the risk/reward calculations are derived exclusively from these transformed values to maintain logic consistency between my manual observations and the EA's backtesting?

I am not looking to share the specific logic of my strategy, just the technical implementation of ensuring the EA consistently "sees" and acts upon the Heikin Ashi data structure.

Any advice on the best data handling approach for this in MQL5 would be greatly appreciated.

I'm looking for feedback on a crypto scalping strategy I recently came across.

The strategy is mainly designed for the 1-minute chart, but the creator also suggests testing it on the 5-minute chart.

Rules:

• Heikin Ashi candles
• EMA 9 on the current chart timeframe
• EMA 50 from the 4H timeframe (used as trend filter)
• RSI with custom levels: 64 / 32 instead of the standard 70 / 30

Long setup:

1. Price is above the 4H EMA 50.
2. RSI drops below 32.
3. A bullish Heikin Ashi candle closes above the EMA 9.
4. Entry must happen within the next two candles.

Short setup:

1. Price is below the 4H EMA 50.
2. RSI rises above 64.
3. A bearish Heikin Ashi candle closes below the EMA 9.
4. Entry must happen within the next two candles.

Risk management:

• Stop loss a few ticks beyond the most recent swing high/low.
• Target around 2:1 RR.
• Move stop to breakeven and take partial profits at 1:1 RR.

The creator claims this works well because all conditions must align before entering a trade.

My questions:

1. What do you think about this strategy from a statistical and practical perspective?
2. Has anyone backtested something similar on the 1-minute or 5-minute timeframe?
3. Do you think the custom RSI levels (64/32) provide any real edge, or is that likely curve fitting?
4. Does anyone here trade a profitable strategy that requires several indicators and confirmations to align before entering? If so, which indicators do you use, and how do you avoid overfitting?

I'm especially interested in hearing from traders who have actually collected data or performed backtests rather than judging it visually on charts.

I’m building a Hermes agent that analyzes marketplace listings. The setup is fairly straightforward: it receives a marketplace link as input, has access to the marketplace API to fetch listing details, and can also use web search. The intended flow is to extract all relevant product information from the link, find comparable listings on the market, and then perform a structured comparison including price, condition, and key specifications. The final output should be a simple score from 0 to 10, where 0 means a bad deal and 10 means a very good deal.

In practice, the agent often doesn’t behave as expected. Sometimes it stops after acknowledging the task, sometimes it asks for confirmation instead of continuing, and other times it ignores the available tools entirely. Even when it does proceed, the output is usually incomplete and lacks proper market comparison.

I’m trying to understand what typically causes this kind of behavior. Is it usually a prompting issue, a tool routing or agent architecture problem, missing constraints in the output format, or something specific to how Hermes handles execution? Any practical debugging advice would help.