Swarm intelligence without degradation using two Qwen models

Hacker News

I recently started a weekend project—partly because I was thinking about cancelling my AI subscriptions—with the goal of creating a system capable of handling long contexts, maintaining persistent memory, and engaging in deep reasoning. To achieve this, I decided to modify a model by structuring its data handling across six axes (details are available on GitHub). In short, by adopting this structure and eliminating unnecessary complexity, the model exchanges information via vectors rather than through natural language—a shift away from the "swarm intelligence" approach used previously. This Space separates the functions into a "thinking model" and a "translating model." Please try entering something into the Space via the link provided. Currently, having lost its spatial anchors, the system has effectively become a machine that outputs philosophical musings. If anyone is able to analyze this model, I would appreciate your insights on how to get it to output coherent, normal text. A key strength of this model—and of "jgen"—is its ability to visualize what the AI is thinking in real-time. https://huggingface.co/spaces/kofdai/Verantyx-God-Mode

Visit product View on Hacker NewsJun 29, 2026

Share card

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, models · Missing: agents, macos, agent

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: visualize, way, para · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

DeepSwarm – Optimising CNNs Using Swarm Intelligence33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

DeepSwarm – Optimising CNNs Using Swarm Intelligence

Hacker News73

Using Qwen to track under-covered news27%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Using Qwen to track under-covered news

Hacker News2

Federalist, an un-censorable browser for the swarm50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Federalist, an un-censorable browser for the swarm

Hacker News8

Rust BPE tokenizer for Qwen models that's 12x faster than HuggingFace66%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Rust BPE tokenizer for Qwen models that's 12x faster than HuggingFace

Hacker News5

I built an O(N) AI using an Agent Swarm. Asking for audit23%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I built an O(N) AI using an Agent Swarm. Asking for audit

Hacker News1

Dante-Qwen-4B – Curing LLM "Neurosis" with a Divine Comedy Curriculum46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Dante-Qwen-4B – Curing LLM "Neurosis" with a Divine Comedy Curriculum

Hacker News5

Docker Swarm 1.12 example using VirtualBox to scale webstack48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Docker Swarm 1.12 example using VirtualBox to scale webstack

Hacker News5

Honeycomb –OS X app for Foursquare/Swarm42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Honeycomb –OS X app for Foursquare/Swarm

Hacker News4

Selling: Ant Swarm Tunnel Warfare $450044%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Ant Swarm Tunnel Warfare - Defense Tech $4500

Indie Hackerscommitment-side-project

The Swarm45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Relationships. Are. Back.

Product Hunt+698Sales

Browse all products Analyze your launch