u/Hot_Country_2177

Has anyone here been following the Yuva Changemakers Leadership Fellowship launched earlier this year by MP Anurag Thakur?

The program was pitched as a way to bring young people from non political backgrounds into public life, offering an annual stipend of 1,21,000 rupees to 21 selected candidates. The idea is that these youths will take on micro projects in their assembly areas and work alongside officials to solve grassroots problems.

I applied for it myself when registrations were open to see how the evaluation process would work, but I have not received any recent updates on the current status of the selections.

What I really want to discuss is the actual motive behind this initiative. What does Anurag Thakur actually gain from this program? By asking thousands of young, ambitious people to register with their district and panchayat details, this looks incredibly like a massive data-harvesting and recruitment drive ahead of the 2027 state elections. Handing out a few stipends is a very cheap way to build a hyper local political machine and identify ground level operators under the completely safe banner of civic engagement and youth empowerment. It feels less like building independent leaders and more like scouting for loyal party cadres.

On the other side, what does Himachal Pradesh really gain from this exercise? We all know the reality of our state infrastructure, where broken roads and administrative delays are just accepted as normal. Throwing a handful of young fellows into the bureaucratic machinery sounds great in a press release, but does it actually force any real accountability? There is a high chance these selected candidates will just be absorbed into standard red tape, taking photographs with local MLAs while the actual systemic issues in our wards remain completely ignored.

If anyone has inside information on whether the exams and evaluations are actually happening, please share the current status.

https://preview.redd.it/r9oowfeoyrah1.png?width=2798&format=png&auto=webp&s=beae6769f8f000226fb64dfc33645fbeb0c82608

A couple of months back, I posted in this subreddit about mapping every broken road, stalled project, and ignored issue in Himachal Pradesh (you might remember the original post: https://www.reddit.com/r/shimla/comments/1sxvtf9/i_am_mapping_every_broken_road_stalled_project/). The response and support I received from you all was solid, and we collected a lot of crucial data from different districts.

The original idea was to take these compiled findings directly to local journalists and tag the secretariat to force some real accountability. Well, I tried exactly that, and it did not go as planned.

Most of the journalists I spoke with were highly critical of the effort. Instead of looking at the objective data, they only wanted to cherry pick and highlight specific problems while completely ignoring others. It became obvious very quickly that their interest was tied to their own political inclinations rather than actual public accountability. If a stalled project did not fit their narrative or attack the right political faction, they simply did not care to report on it.

Since we cannot rely on the traditional media to present an unbiased, unfiltered picture of our state's crumbling infrastructure and neglected issues, we are going to do it ourselves.

I have officially launched the live web tracker for the Himachal Accountability Project (HAP). You can view the compiled data, see the real picture, and track the progress here: https://himachal-accountability-project.vercel.app/

Because I am managing this independently and bypassing the media filters, I am looking for volunteers to help me scale this up. If you care about demanding better for our state and have some free time to help verify form submissions, manage the data on the platform, website and dashboard management and development, or just help gather local reports from your area, please drop a comment or send me a DM.

Let me know what you think of the new site, and keep the reports coming.

PS: It's still in development, so please excuse any bugs for now.

https://preview.redd.it/r87e1sc9nrah1.png?width=2792&format=png&auto=webp&s=c6b217284923d37484f4ebbba58bbea43dcd2926

A couple of months back, I posted in this subreddit about mapping every broken road, stalled project, and ignored issue in Himachal Pradesh (you might remember the original post: https://www.reddit.com/r/HimachalPradesh/s/7QQVlLCYtD). The response and support I received from you all was solid, and we collected a lot of crucial data from different districts.

The original idea was to take these compiled findings directly to local journalists and tag the secretariat to force some real accountability. Well, I tried exactly that, and it did not go as planned.

Most of the journalists I spoke with were highly critical of the effort. Instead of looking at the objective data, they only wanted to cherry pick and highlight specific problems while completely ignoring others. It became obvious very quickly that their interest was tied to their own political inclinations rather than actual public accountability. If a stalled project did not fit their narrative or attack the right political faction, they simply did not care to report on it.

Since we cannot rely on the traditional media to present an unbiased, unfiltered picture of our state's crumbling infrastructure and neglected issues, we are going to do it ourselves.

I have officially launched the live web tracker for the Himachal Accountability Project (HAP). You can view the compiled data, see the real picture, and track the progress here: https://himachal-accountability-project.vercel.app/

Because I am managing this independently and bypassing the media filters, I am looking for volunteers to help me scale this up. If you care about demanding better for our state and have some free time to help verify form submissions, manage the data on the platform, website and dashboard management and development, or just help gather local reports from your area, please drop a comment or send me a DM.

Let me know what you think of the new site, and keep the reports coming.

PS: It's still in development, so please excuse any bugs for now.

The state government is running on a practically empty treasury. They invoked Article 309 last month to defer 30 percent of the salaries for top officers just to keep the lights on. HRTC drivers and conductors are practically on the streets begging for their basic salaries and pending night overtime. And ofc the Debt/GDP ratio is an all time high

Meanwhile, your local MLA is essentially a decoration now. For those of you who dont know, they used to be able to push 5 major priority projects a year, but now they are restricted to exactly 1 project per FY. Their local repair limits got slashed from 10 to 12 maintenance works down 4. They literally do not have the B udget or legal authority to fix the roads in your constituency even if they wanted to.

And for anyone thinking a BJP sweep will magically save us, look at what we got in b udget this FY. We gave the BJP all 4 Members of Parliament (All are absolute trash. I haven't seen anyone participate in parliament proceedings ever except for Kangana sometimes). In return, the Center abruptly cut our Revenue Deficit Grant and threw the state into a 10,000 crore black hole. While our actual highways and local infrastructure crumble, the 2026 Union b udget gave HP absolute peanuts like "walking trails" or sumshit.

So with Congress bankrupting the state and BJP completely abandoning us from the Center, who are you actually voting for in 2027? Who do you realistically think is going to win your constituency, and why? Are we just picking the lesser evil again, or is there any actual independent or new leadership stepping up?

Tired of paying CAs ₹3,000–15,000 for a straightforward salaried return, I spent the last few months building a tool.

How it works: you chat with it like WhatsApp. It asks plain, English questions, no forms, no jargon. Upload your Form 16 and it pre fills most of it. Takes about 20 minutes. At the end it generates your ITR XML ready to upload to the income tax portal.

Covers: salaried income, HRA, home loan, 80C/80D, capital gains on MF/stocks, NRI returns.

Price: ₹499 one time per filing year.

Genuinely want to know:

1. Would you use this or do you prefer a CA/ClearTax?

2. Does ₹499 feel right, too high, or too low?

3. Would you trust an AI with your PAN and salary details?

Not selling anything yet, still in beta. Just want honest feedback before I launch.

Hey everyone,

I’m currently building a fintech venture focused on credit modeling using the Account Aggregator framework, and I hit a massive bottleneck: the raw transaction data from banks is an absolute nightmare.

Whether it's UPI, NEFT, or standard POS swipes, parsing strings like UPI/ZOMATO/123456/PAYMENT or POS/DOMINOS/NEW DELHI into usable data requires writing insane custom rules. Trying to pass thousands of these raw strings into an LLM completely blows up the context window, introduces hallucinations, and spikes costs.

Because I need this for my own risk engine, I’m spinning out the core parsing logic into a standalone API designed explicitly for automated workflows, AI agents, and fintech dashboards.

Here is exactly what it does:

You send it a batch of messy transaction strings or a raw CSV export.

Instead of returning a wall of text, it instantly cleans it and gives you back structured data. For example, if you send it UPI/SWIGGY/987654321/OrderPayment, it tells you:

• The exact merchant is Swiggy.
• The category is Food & Beverage.
• The transaction type is a Debit.
• And it gives a Confidence Score so you know how accurate the categorization is.

How it works under the hood: It’s completely headless, no clunky dashboard, no UI. It uses a heavily optimized Python rule engine to handle 90% of the cleaning locally in milliseconds (so there is zero AI latency or high compute cost). It only falls back to a lightweight model for the weird, edge case transactions. It's built for machines to read and use instantly.

I have three questions for founders and builders in this space:

1. Is this a hair on fire problem for you? Are you currently wrestling with raw bank statement parsing for automated bookkeeping, expense tracking, or credit models?
2. Pricing model: Because this is built for automated systems, I’m planning to charge a fraction of a cent per successful categorization rather than a flat monthly subscription. Does this align with how you prefer to buy software?
3. Missing pieces: What is the one weird data point or edge case that standard bank parsers always get wrong that you'd want this to solve?

Any brutal feedback is welcome before I deploy. Thanks!

https://preview.redd.it/q16r1svpfwyg1.png?width=2816&format=png&auto=webp&s=538312a9c4314011e9280a9fb96bc5450eca9430

Hey everyone,

I’m currently building a fintech venture focused on credit modeling using the Account Aggregator framework, and I hit a massive bottleneck: the raw transaction data from banks is an absolute nightmare.

Whether it's UPI, NEFT, or standard POS swipes, parsing strings like UPI/ZOMATO/123456/PAYMENT or POS/DOMINOS/NEW DELHI into usable data requires writing insane custom rules. Trying to pass thousands of these raw strings into an LLM completely blows up the context window, introduces hallucinations, and spikes costs.

Because I need this for my own risk engine, I’m spinning out the core parsing logic into a standalone API designed explicitly for automated workflows, AI agents, and fintech dashboards.

Here is exactly what it does:

You send it a batch of messy transaction strings or a raw CSV export.

Instead of returning a wall of text, it instantly cleans it and gives you back structured data. For example, if you send it UPI/SWIGGY/987654321/OrderPayment, it tells you:

• The exact merchant is Swiggy.
• The category is Food & Beverage.
• The transaction type is a Debit.
• And it gives a Confidence Score so you know how accurate the categorization is.

How it works under the hood: It’s completely headless, no clunky dashboard, no UI. It uses a heavily optimized Python rule engine to handle 90% of the cleaning locally in milliseconds (so there is zero AI latency or high compute cost). It only falls back to a lightweight model for the weird, edge case transactions. It's built for machines to read and use instantly.

I have three questions for founders and builders in this space:

1. Is this a hair on fire problem for you? Are you currently wrestling with raw bank statement parsing for automated bookkeeping, expense tracking, or credit models?
2. Pricing model: Because this is built for automated systems, I’m planning to charge a fraction of a cent per successful categorization rather than a flat monthly subscription. Does this align with how you prefer to buy software?
3. Missing pieces: What is the one weird data point or edge case that standard bank parsers always get wrong that you'd want this to solve?

Any brutal feedback is welcome before I deploy. Thanks!

Hey everyone,

I’m currently building a fintech venture focused on credit modeling using the Account Aggregator framework, and I hit a massive bottleneck: the raw transaction data from banks is an absolute nightmare.

Whether it's UPI, NEFT, or standard POS swipes, parsing strings like UPI/ZOMATO/123456/PAYMENT or POS/DOMINOS/NEW DELHI into usable data requires writing insane custom rules. Trying to pass thousands of these raw strings into an LLM completely blows up the context window, introduces hallucinations, and spikes costs.

Because I need this for my own risk engine, I’m spinning out the core parsing logic into a standalone API designed explicitly for automated workflows, AI agents, and fintech dashboards.

Here is exactly what it does:

You send it a batch of messy transaction strings or a raw CSV export.

Instead of returning a wall of text, it instantly cleans it and gives you back structured data. For example, if you send it UPI/SWIGGY/987654321/OrderPayment, it tells you:

• The exact merchant is Swiggy.
• The category is Food & Beverage.
• The transaction type is a Debit.
• And it gives a Confidence Score so you know how accurate the categorization is.

How it works under the hood: It’s completely headless, no clunky dashboard, no UI. It uses a heavily optimized Python rule engine to handle 90% of the cleaning locally in milliseconds (so there is zero AI latency or high compute cost). It only falls back to a lightweight model for the weird, edge case transactions. It's built for machines to read and use instantly.

I have three questions for founders and builders in this space:

1. Is this a hair on fire problem for you? Are you currently wrestling with raw bank statement parsing for automated bookkeeping, expense tracking, or credit models?
2. Pricing model: Because this is built for automated systems, I’m planning to charge a fraction of a cent per successful categorization rather than a flat monthly subscription. Does this align with how you prefer to buy software?
3. Missing pieces: What is the one weird data point or edge case that standard bank parsers always get wrong that you'd want this to solve?

Any brutal feedback is welcome before I deploy the MVP this weekend. Thanks!

PS: Post is written by AI so don't eat me for it in the comments.

Hey everyone,

I’m currently building a fintech venture focused on credit modeling using the Account Aggregator framework, and I hit a massive bottleneck: the raw transaction data from banks is an absolute nightmare.

Whether it's UPI, NEFT, or standard POS swipes, parsing strings like UPI/ZOMATO/123456/PAYMENT or POS/DOMINOS/NEW DELHI into usable data requires writing insane custom rules. Trying to pass thousands of these raw strings into an LLM completely blows up the context window, introduces hallucinations, and spikes costs.

Because I need this for my own risk engine, I’m spinning out the core parsing logic into a standalone API designed explicitly for automated workflows, AI agents, and fintech dashboards.

Here is exactly what it does:

You send it a batch of messy transaction strings or a raw CSV export.

Instead of returning a wall of text, it instantly cleans it and gives you back structured data. For example, if you send it UPI/SWIGGY/987654321/OrderPayment, it tells you:

• The exact merchant is Swiggy.
• The category is Food & Beverage.
• The transaction type is a Debit.
• And it gives a Confidence Score so you know how accurate the categorization is.

How it works under the hood: It’s completely headless, no clunky dashboard, no UI. It uses a heavily optimized Python rule engine to handle 90% of the cleaning locally in milliseconds (so there is zero AI latency or high compute cost). It only falls back to a lightweight model for the weird, edge case transactions. It's built for machines to read and use instantly.

I have three questions for founders and builders in this space:

1. Is this a hair on fire problem for you? Are you currently wrestling with raw bank statement parsing for automated bookkeeping, expense tracking, or credit models?
2. Pricing model: Because this is built for automated systems, I’m planning to charge a fraction of a cent per successful categorization rather than a flat monthly subscription. Does this align with how you prefer to buy software?
3. Missing pieces: What is the one weird data point or edge case that standard bank parsers always get wrong that you'd want this to solve?

Any brutal feedback is welcome before I deploy the MVP this weekend. Thanks!

PS: Post is written by AI so don't eat me for it in the comments.