u/Tutyfruiity

So I have a VPS, and I self-host a lot of services, but I am unable to access my Jellyfin and Navidrome, etc through my college Wi-Fi since they use Fortinet, and all the services I hosted are configured in a way that I can access them via Tailscale. Is there any way to access my service without making it public (mobile data is weak in my college area)

So I have a VPS, and I self-host a lot of services, but I am unable to access my Jellyfin and Navidrome, etc through my college Wi-Fi since they use Fortinet, and all the services I hosted are configured in a way that I can access them via Tailscale. Is there any way to access my service without making it public (mobile data is weak in my college area)

Component	Item	Price
CPU	AMD RYZEN 5 3400G 2ND Generation with 4 Core, up to 4.2 GHZ, AM4 Socket, 6MB Cache (YD3400C5FHBOX)	₹9,399
Motherboard	ASUS Prime A520M-K (AMD AM4 DDR4 Socket for AMD Ryzen 5000/5000 G/ 4000 G/ 3000) Micro ATX Motherboard with M.2 Support 1Gb Ethernet HDMI/D-Sub SATA 6Gbps and USB 3.2 Gen 1 Type-A	₹5,419
RAM	Geonix 8GB DDR4 Desktop RAM 3200 Mhz, 5 Years Warranty	₹3,099
Storage	Consistent 512GB 2.5 Inch SATA SSD	₹5,300
PSU	Zebronics 450W Black Gold Power Supply with 3X SATA connectors, 2X Molex, 80mm Built-in Fan, Extra Long Cables, 1.2 Meter Power Cable and Suitable for Regular use Computers	₹799
Cabinet	[Frontech Mystic Silver Computer Cabinet Compact PC Case for Home & Office	₹809
Monitor	ZEBRONICS 21.5” (54.6cm) LED Monitor, FHD 1920x1080, 250nits Brightness, Dual Input - HDMI/VGA, 2000000:1 Dynamic Contrast Ratio, 16.7M Colors, 16:9 Aspect Ratio, Wall Mountable (EA 122)	₹3,999
Peripherals	HP 115 Wired Keyboard and Mouse Combo	₹799
UPS	ZEBRONICS Zeb-U725 600VA UPS	₹ 2299
Speakers	ZEBRONICS Fame 2.0 USB Speakers (5 Watts)	₹ 500
Total		₹32,422

is this okay? or should i change anything, trying to keep it under 30k.

Component	Item	Price
CPU	Core i7 4th Gen 4770 (3.4 GHz - 3.9 GHz, LGA 1150)	₹4,199
Motherboard	FRONTECH H81 Chipset Motherboard (LGA1150, DDR3)	₹1,990
CPU Cooler	Standard 4-Pin Desktop CPU Cooling Fan (Aluminium Heatsink)	₹299
RAM	Crucial 8GB DDR3 1600 MHz Desktop Memory	₹4,780
Storage	Consistent 512GB 2.5 Inch SATA SSD	₹5,300
PSU	Cooler Master MWE 450 Bronze V2 (450 Watt)	₹3,349
Cabinet	Frontech Warrior Gaming Mid-Tower PC Case (Acrylic Panel)	₹1,329
Monitor	Zebronics 21.5" Full HD LED Monitor (100Hz, Bezel-Less)	₹5,499
Peripherals	HP 115 Wired Keyboard and Mouse Combo	₹799
UPS	ZEBRONICS Zeb-U725 600VA UPS	₹ 2299
Speakers	ZEBRONICS Fame 2.0 USB Speakers (5 Watts)	₹ 500
Total		₹30,343

Is this okay or should i make any changes,our budget is ₹30k

So njan oru textile shop ill partime job nokanu(4hr/day 4days off monthly)and they asked "whats the expected salary that im looking?". idk what to say pls help me 😭🙏🏽🙏🏽

I've been building a multi-agent personal finance tracker in n8n (Telegram → main agent → sub-agents → MongoDB). The sub-agent responsible for DB retrieval is giving me wrong/hallucinated results

Architecture:

Main agent — classifies user intent, picks the correct sub-agent, passes structured instructions (intent type, user_id, date range, filters)

MongoDB sub-agent — receives that structured input, builds + runs an aggregation pipeline, returns structured JSON back to main agent

DB: MongoDB, collection: transactions

What I've already done in the sub-agent system prompt

1. Full schema provided — every field, its type, allowed values, and constraints (e.g. amount_paise is the only money field, divide by 100 for INR, user_id is always an integer)
2. 15 intent types defined with explicit routing rules (e.g. monthly_summary, person_cash_flow, investment_tracking, savings_rate, comparison, etc.) and clear disambiguation rules like "if a specific person's name appears → always person_cash_flow, never any other transfer intent"
3. Pre-built pipeline templates for every intent — the agent isn't meant to write pipelines from scratch. Pipeline A through O are all written out. It just fills in variables like user_id, year, month
4. Hard arithmetic rules — the agent is forbidden from doing any INR math itself. All division happens inside $divide:["$amount_paise",100] within the pipeline. Only 3 post-pipeline calculations are permitted (savings_rate, savings_inr, comparison delta)
5. Strict $match ordering — user_id must always be the first field in every $match
6. Timeframe rules — agent uses pre-computed year/month/day/week_key integer fields, never parses date strings
7. Empty result handling — explicit fallback rules for every pipeline type (empty [] → return zeroed-out JSON, not an error or hallucination)
8. Strict output format — output is JSON only, schema defined per intent, no markdown, no explanation
9. Optional filter injection rules — how to handle amount ranges, platform normalization (always lowercase), regex for item_name/note search, etc.

Despite all this, the agent still:

• Reports transactions as non-existent when they clearly exist
• Returns wrong totals (e.g. investments total comes back as 0 when there are matching records)
• Occasionally returns a well-formatted JSON response with numbers it just made up
• Sometimes runs the right pipeline intent but with a broken $match (my suspicion)

Example(a valid case):

Main agent passes:

Find total investment transactions for May 2026, user_id, aggregation type: monthly_summary, filter: type=investment, date range: 2026-05-01 to 2026-05-31

Sub-agent returns a valid-looking monthly_summary JSON with investments: {total_inr: 6798, count: 1}

I've been building a multi-agent personal finance tracker in n8n (Telegram → main agent → sub-agents → MongoDB). The sub-agent responsible for DB retrieval is giving me wrong/hallucinated results

Architecture:

Main agent — classifies user intent, picks the correct sub-agent, passes structured instructions (intent type, user_id, date range, filters)

MongoDB sub-agent — receives that structured input, builds + runs an aggregation pipeline, returns structured JSON back to main agent

DB: MongoDB, collection: transactions

What I've already done in the sub-agent system prompt

1. Full schema provided — every field, its type, allowed values, and constraints (e.g. amount_paise is the only money field, divide by 100 for INR, user_id is always an integer)
2. 15 intent types defined with explicit routing rules (e.g. monthly_summary, person_cash_flow, investment_tracking, savings_rate, comparison, etc.) and clear disambiguation rules like "if a specific person's name appears → always person_cash_flow, never any other transfer intent"
3. Pre-built pipeline templates for every intent — the agent isn't meant to write pipelines from scratch. Pipeline A through O are all written out. It just fills in variables like user_id, year, month
4. Hard arithmetic rules — the agent is forbidden from doing any INR math itself. All division happens inside $divide:["$amount_paise",100] within the pipeline. Only 3 post-pipeline calculations are permitted (savings_rate, savings_inr, comparison delta)
5. Strict $match ordering — user_id must always be the first field in every $match
6. Timeframe rules — agent uses pre-computed year/month/day/week_key integer fields, never parses date strings
7. Empty result handling — explicit fallback rules for every pipeline type (empty [] → return zeroed-out JSON, not an error or hallucination)
8. Strict output format — output is JSON only, schema defined per intent, no markdown, no explanation
9. Optional filter injection rules — how to handle amount ranges, platform normalization (always lowercase), regex for item_name/note search, etc.

Despite all this, the agent still:

• Reports transactions as non-existent when they clearly exist
• Returns wrong totals (e.g. investments total comes back as 0 when there are matching records)
• Occasionally returns a well-formatted JSON response with numbers it just made up
• Sometimes runs the right pipeline intent but with a broken $match (my suspicion)

Example(a valid case):

Main agent passes:

Find total investment transactions for May 2026, user_id, aggregation type: monthly_summary, filter: type=investment, date range: 2026-05-01 to 2026-05-31

Sub-agent returns a valid-looking monthly_summary JSON with investments: {total_inr: 6798, count: 1}

Guys os exam change akkile 17th in? Circular onnum kandilalo🥲