As a newbie in the world of entrepreneurship, I tried outbound sales for 3 months with my first venture. Was never worth the effort. In my opinion, it is extremely difficult to sell to people who aren't looking for the stuff or are interested in the stuff you are selling.

Neovim with a few extensions installed by default. What other features does it have?

Thats a strong RL technique that could equal the quality of RLHF.

A platform that takes your podcast footage and produces the podcast(with trailer), mid form clips and reels by analyzing what your audience responds to posts it on various social media[0].

A fiat to crypto payment gateway for businesses and freelancers without a strict KYC. Users can pay using card and merchants can claim instant crypto settlement[1].

WIP: a casino algorithm that outperforms most casino algorithms in terms of user retention over a long period of time with the objective function of maximizing long term profit.

[0]: https://xclip.in [1]: https://obliqpay.com

I agree with you about writing. Back in 2020, I made a commitment to study a CS or math topic in detail each week and then write an essay about it. Those were some of my best learning experiences and when I look back at those essays, they are pure gold.

You could create a browser extension that normal users could install such would warn them of a phishing site or email from that domain. It would be 0 cost since you already have the data.

I know 4 languages. 3 of those I learnt because of my family. I learnt Russian because of work (+fun). I feel that it is always best to go the classic route and learn a language from a manual (currently learning mandarin from a manual) and that gamified experiences of learning languages have a very low learning/effort ratio.

You can use the classic route in my app. You can browse Kanji characters sequentially and memorize them.

I agree that over-gamified experiences are detrimental. That's why I try to build features that help people immerse themselves in the language.

Interested. Could you tell me more about it?

How is it different from croc?

From what I understand, croc is P2P, i.e. both computers have to be on for the transfer to happen (the "relay" that they mention only helps negotiate the connection between two peers). With aero.zip, you upload your files to a server, and the recipient can download it whenever - either real-time while you're still uploading them (imitating the P2P/croc model), or at a later date. This is a more universal approach IMHO.

Also, aero.zip is a webapp, i.e. there's nothing to install, and you don't even need to sign up to send small files. Meanwhile, croc is a CLI utility which will be hard to use by mom-and-pop users.

Got it. Is it safe to say that aero.zip is closer to wetransfer than it is to croc?

Is it open source?

It's not ready for prime-time yet, so not open-source at the moment. I've managed to have it search over 9 asset types, including Crypto, stocks (e.g. different categories), but it is slow going as I am using a free-teer AI. It's found some stuff on the net that is barely in the pre-sale phase, and it predicts 10,000% gain but don't know if it is an accurate prediction or a massive hallucination. Also, I know this has the potential to be a money maker, but I don't have neither the experience nor the financial resources to host this online or make this a viable money-maker at the moment, unfortunately :(

Have you tried handing it over some test money on a trading platform to see the results? What prediction algorithm is it using?

>>Have you tried handing it over some test money on a trading platform to see the results?

Not everybody has test money to play with. I'm in that group due to the crap economy left behind by previous politicians.

>> What prediction algorithm is it using?

As money is an issue, all I have at the moment is Google's AI Studio which is definitely not opensource, in my view. Though it is more forgiving than other vibe tools out there with their usage quotas. The market predictor has no capabilities to connect to any proprietary APIs as that requires money. That being said, it should be possible to modify the code so that it uses proprietary APIs should anyone want to do it. For now, I'm just adding stuff to it via VibeCoding that I think could be helpful. The main goal is to turn it into a research App that finds potential assets that will double to triple in the near feature or stocks that are prime for shorting, etc. I am also thinking of adding investment simulation capabilities but not quite sure how to go about at the moment. Turning some of its capabilities as a learning platform could be a good way to monetize the app. Not really a serious app at this moment, though, as I lack lots of tools to accomplish this.

Based on the code provided in services/geminiService.ts, here is the breakdown of the algorithms and sources used by the market prediction AI application:

Prediction Algorithm: The application does not use traditional quantitative statistical models (like ARIMA, LSTM, or Linear Regression) running on raw numerical data. Instead, it uses a Generative AI / Large Language Model (LLM) approach:

Model: The app utilizes Google's Gemini 2.5 Flash (gemini-2.5-flash).

Methodology: Context Gathering: The AI first performs a real-time Google Search (tools: [{ googleSearch: {} }]) to gather the latest text-based data, news, sentiment, and technical analysis summaries available on the web.

Semantic Analysis: The AI acts as a "Senior Financial Analyst" to interpret this unstructured data. It synthesizes a sentiment score (0-100) and predicts future price targets based on the qualitative data found (news catalysts, earnings, etc.).

Chart Generation: --The historical and forecast charts are generated mathematically within the code using Deterministic Linear Interpolation. --It connects the current price to the AI's predicted future price targets (1 week, 1 month, etc.).

--It adds algorithmic "noise" (randomness seeded by the asset name and date) to simulate market volatility visually, ensuring the chart looks realistic but stable for the specific day.

Data Sources: --The application relies entirely on Google Search Grounding. It does not connect to specific hardcoded financial APIs (like Bloomberg or Yahoo Finance API directly). Instead, it instructs the AI to search the public web for specific types of information.

Based on the prompts defined in the code, here are the sources and data points the app targets:

Real-Time Aggregators & Search Engine Results: --Live Google Search results for real-time price estimates. --Global financial news outlets (indexed by Google). --Market sentiment analysis from web summaries.

Regulatory & Official Documents: --SEC EDGAR Database: Specifically targeted in prompts for Stocks, Quantum, and Cannabis strategies to find filings.

Company Press Releases: --Used to identify fresh catalysts like contracts or product launches.

Technical Data: --Support and Resistance levels (retrieved from technical analysis articles found via search). --Relative Volume (RVOL) and Intraday Volatility data (for Day Trading strategy). --Chart patterns (Bull Flags, Pennants, Opening Range Breakouts).

Sector-Specific Sources: --Crypto Launchpads: Seedify, DAO Maker, Polkastarter (specifically for the "AI Presale" strategy).

Venture Capital Reports: --Data on VC investments and insider buying.

Industry News: Specific searches for Quantum Computing breakthroughs, Cannabis legalization news, and AI technology updates.

Market Dynamics: --Short interest data and borrow fees (for the "Short" strategy). --Analyst upgrades/downgrades and price targets. Macro-economic trend reports.

As I mentioned earlier, it actually found lots of legit assets with breakout potential but also finds crypto asset for that I suspect are scams but have a presence on the web.

Hope that provides a bit of context to your query.