u/Dense-Map-406

I’m curious what free marketing strategies have actually worked for other solo founders and marketers.

Reddit posts? Cold outreach? SEO? Community building? Partnerships? Something more unconventional?

What did you try, and how did you measure whether it was genuinely successful rather than just generating views or engagement?

Would love to hear real examples, including what you were promoting and the results you saw.

Are local models today good enough and small enough that can enable using Hermes locally without api from frontier ones?

Hey there Redditors of Tel Aviv

I’ve been building this app and I’m constantly looking for people to talk with and learn from their stories on how to improve it

Looking for like minded people that are also developing their own things to perhaps share stories and lessons learned

Thanks!

So I made an app called Glance, I’m a tech guy, looking into a new campaign where I’m open to collating with a person excelling in sales and give him/her 90% of profits for onboarded paying users

How does that model sound? How can I find a person for this?

For added context, my app is called glance and it has a pretty cool concept it lets you post, share, and follow content on iOS Home Screen widgets

So there are 2 ICPs currently, users that want to create public widgets to share with their audience whether if it’s course teachers want to add a cool tool for students to get micro learning widgets, content creators that want to share content on home screens and more. The second is builders and Automators or teams that want to create dashboards on widgets either for themselves or for a team

I’d love to hear recommendations!

Thanks

I have been thinking about an alternative tokenization and representation scheme for language models and would be interested in hearing whether similar ideas have been explored before, as well as potential advantages or flaws.

The core observation is that modern tokenizers (BPE, SentencePiece, etc.) primarily capture statistical structure in text. While this is highly effective, the resulting token assignments are not explicitly organized according to semantic relationships. Concepts that are semantically related may end up with completely unrelated token identifiers, and semantic structure is learned later through embeddings and training.

The idea is to construct a tokenization scheme in which the symbolic representation itself carries semantic information.

For example, instead of assigning arbitrary identifiers to concepts, we could learn a mapping from concepts to short character strings such that semantically similar concepts receive similar codes. A concept like “dog” might receive a code close to those assigned to “wolf” and “fox”, while more distant concepts such as “car” would receive codes that are farther away in the code space.

One possible implementation would be:

1. Build a semantic graph using resources such as WordNet, embedding similarity, or a combination of both.
2. Learn a compact symbolic encoding for concepts.
3. Optimize the encoding so that distances between codes correlate with semantic distances in the graph.
4. Train language models directly on these codes.

An extension of the idea is to treat a standard keyboard layout as a fixed geometric space. The keyboard itself is not semantically meaningful, but it provides a globally agreed-upon metric structure. The learned encoding could exploit distances between characters and positions when constructing semantic codes.

For example, if two concepts are semantically close, their symbolic representations would differ only slightly. Ambiguous concepts could potentially occupy positions that reflect their relationships to multiple semantic regions. Context would still determine the intended meaning, but the representation itself would encode semantic structure rather than relying entirely on downstream embedding learning.

My intuition is that such a representation could act as an inductive bias, potentially improving:

- Sample efficiency
- Training efficiency
- Interpretability
- Cross-lingual concept sharing
- Compression of semantic information

However, it is also possible that sufficiently large models already learn these structures efficiently, making such an encoding unnecessary.

I would be interested in feedback on several questions:

1. Has similar work been explored in tokenization, representation learning, or NLP?
2. Are there theoretical reasons why such a representation should or should not help?
3. Would a semantically structured symbolic space provide a useful inductive bias for transformer-based models?
4. Are there related approaches involving semantic hashing, vector quantization, discrete latent spaces, graph embeddings, or other forms of structured tokenization that I should look into?

I am particularly interested in understanding whether explicitly embedding semantic structure into the symbolic representation could provide measurable benefits over learning that structure entirely through embeddings and model training.

So it’s been 100 days since I released my, Glance, just me myself and I.. following several months of planning and developing .. honestly a really cool experience I finally got to see how it feels to have active users engaging with what my app offers

I’ve been attempting to look for users that might be interested in paying for the premium features but I guess that’ll happen whenever it happens

I’m fully enjoying the ride of slowly releasing more and more features requested by the users themselves, discussing what they’ve done with the app and actually I’ve been using it myself

So I guess I can determine it as a success but I would like the next step.. but as I see it I don’t think it’s going to be still a solopreneur story since distribution isn’t my forte

I’ll be looking for a partner to manage that side while I control the Dev

Altogether love it it’s really fun 🤩

Hey every one :) was interested in a question.. I’m currently on my 4th project that I’m trying to get going with the previous three failing ..

This one looks to be a bit better I’m already getting traction and people are enjoying the product

Got me thinking.. how many projects did you go through until something worked? Was it all in the same sector? Different variation of the same idea? Or completely different?

For some context :

My first project was a clothing brand and age 16.. then an appointment booking platform at 18.. after that a survey widget software web apps.. and after that a coupon code sharing platform (still live but not showing interesting signals) and today I’m working on Glance.. it’s a widget infrastructure for businesses or social individuals that want to offer content to visualize natively on mobile widgets without the needs for apps

What have you build and where are you today?