Vori

Learn everything you need to know about Decentralized Web via these 77 free HackerNoon stories.

77 Stories To Learn About Decentralized Web

Interview David Vorick CEO of Skynet Labs about trends, pitfalls, why it’s important to own the data and how Homescreen's going to change the way we use the Web

An Introduction to Skynet's Homescreen for Managing DeFi and DWeb Apps 

Skynet is an open protocol for building and hosting decentralized applications. It is built on Sia, a decentralized storage network.

Web Monetization In Crypto: The Skynet Way

Monero (XMR) and ZCash (ZEC) are the two most popular privacy-centric peer-to-peer crypto-networks of confidential value circulation (or crypto-hawalas, if you will). Although both in practice conceal sender, receiver, amounts and balances, they implement very different mechanisms and protocols and work in altogether different ways to achieve that. We've already covered both in two separate articles, but below we'll make a brief comparison between the two to get a better understanding of their properties in order to make more informed choices when to use which and the particular purposes best suited for each one of them.

Monero Vs. ZCash: The Definitive Comparision

[Note: This work was written and self-published in manuscript form on <a href="http://pllel.com" target="_blank">pllel.com</a> in summer 2018 with <a href="http://www.pllel.com/industries/wp-content/uploads/180810-forkonomy-v3.pdf" target="_blank">last revision 10th August</a>. Figures come from tweets, original manuscript and presentation slides from <a href="https://www.youtube.com/watch?v=d_7f16_29Mc" target="_blank">ETC Summit Forkonomy talk</a> in September 2018. <a href="https://hackernoon.com/forkonomy-revisited-where-are-they-now-73fbfbec6b4d" target="_blank">A follow-up commentary was just published</a>. TLDRs in video and tweet form, scroll down for the full text.]

Towards an Analytical Discipline of Forkonomy (Summer 2018)

<a href="https://berminal.com/coins/Bitcoin-BTC" target="_blank">BTC</a>: $6,562.60 (-0.41%) <a href="https://berminal.com/coins/Ethereum-ETH" target="_blank">ETH</a>: $223.33 (-0.67%) <a href="https://berminal.com/coins/XRP-XRP" target="_blank">XRP</a>: $0.475 (-3.68%)

Coinbase Valued At $8 Billion And Ripple’s XRapid Goes Into Commercial Production

The State of The Market: Today was another slow day for the market, as Bitcoin went from $6,500 to $6,610, only to fall back to $6,500 once again. The 24-hour change for <a href="https://berminal.com/coins/Bitcoin-BTC" target="_blank">Bitcoin</a> is just -0.16%. Ethereum has also been stable at $228. However, XRP, which went up by more than 100% last week in anticipation of its invite-only event which took place today, did not move. It is still at $0.56, though the company announced new partnerships at the event for its xRapid product.

Ripple’s XRapid Goes Into Commercial Production And Ethereum Enterprise Alliance Partners With…

For my first review I will be going over Sia — one of the players in the decentralized storage space. The cloud storage industry is massive and companies like Sia are hoping to put a dent in the dominance Amazon, Microsoft, and Google have enjoyed over the past decade.

Crypto Review — Siacoin (SC)

Voice is the next big data problem. See how Deepgram masters noisy audio and why the future of real time AI requires ethical voice cloning.

The Sound of Data: Why Your Voice is the Hardest Problem in AI

Voice AI explained by someone who's built with it — the tech, the business, the ethics, and what nobody's talking about yet.

Voice AI: Here's What You Need to Know About It

Learn how fal-ai’s qwen-3-tts/voice-design/1.7b generates custom voices from natural-language descriptions across 10 major languages, with WAV output.

Design a Voice With Words: Inside fal-ai’s qwen-3-tts/voice-design/1.7b

This is a simplified guide to an AI model called LuxTTS [https://www.aimodels.fyi/models/huggingFace/luxtts-yatharths?utm_source=hackernoon&utm_medium=referral] maintained by YatharthS [https://www.aimodels.fyi/creators/huggingFace/YatharthS?utm_source=hackernoon&utm_medium=referral]. If you like these kinds of analysis, join AIModels.fyi [https://www.aimodels.fyi/?utm_source=hackernoon&utm_medium=referral] or follow us on Twitter [https://x.com/aimodelsfyi].


MODEL OVERVIEW

LuxTTS is a lightweight text-to-speech model designed for efficient voice cloning and realistic speech generation. Built on ZipVoice and distilled to 4 steps, it delivers performance comparable to models ten times larger while maintaining a minimal footprint. Unlike MiraTTS [https://aimodels.fyi/models/huggingFace/miratts-yatharths?utm_source=hackernoon&utm_medium=referral], which achieves speeds of 100x realtime, LuxTTS reaches speeds exceeding 150x realtime on a single GPU. The model prioritizes accessibility by fitting within 1GB of VRAM, making it viable for any local GPU setup.


MODEL INPUTS AND OUTPUTS

LuxTTS accepts text input along with a reference voice sample for cloning, then generates high-fidelity speech audio. The model produces clear 48kHz audio output, which represents a quality advantage over standard TTS models limited to 24kHz. The implementation uses an advanced sampling technique that improves upon standard euler sampling, resulting in more natural prosody and audio clarity.


INPUTS

 * Text prompt: The speech content to generate
 * Voice sample: Reference audio for voice cloning


OUTPUTS

 * 48kHz audio: Clear speech synthesis matching the reference voice characteristics


CAPABILITIES

The model excels at voice cloning with state-of-the-art results despite its compact size. It generates speech with clarity and naturalness, producing 48kHz audio quality. Speed is a significant capability, reaching 150x realtime on GPUs and faster-than-realtime performance on CPUs. The efficient design means voice cloning operations consume minimal computational resources while maintaining quality standards previously associated with much larger models.


WHAT CAN I USE IT FOR?

Voice cloning applications benefit from LuxTTS in scenarios requiring fast, high-quality synthesis on resource-constrained hardware. Content creators can synthesize voiceovers for videos or podcasts with custom voice cloning. Interactive applications can integrate real-time speech generation for chatbots or virtual assistants without requiring expensive cloud API calls. Accessibility tools can provide personalized text-to-speech for users who want custom voices. The model's efficiency makes it suitable for edge deployment on personal devices where privacy and low latency matter.


THINGS TO TRY

Test the model with short voice samples to understand how minimal audio input produces quality cloning results. Experiment across different text content to observe how prosody adapts while maintaining voice consistency. Compare synthesis speed between GPU and CPU execution to identify optimal deployment scenarios for your hardware constraints. Explore the model's behavior with longer passages to assess how voice characteristics persist across extended speech generation. Try using the provided Colab Notebook for quick experimentation before setting up local deployment.

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Original post: Read on AIModels.fyi [https://www.aimodels.fyi/models/huggingFace/luxtts-yatharths?utm_source=hackernoon&utm_medium=referral]

LuxTTS: Lightweight Voice Cloning That Fits in 1GB VRAM

This is a simplified guide to an AI model called Qwen3-TTS-12Hz-1.7B-CustomVoice [https://www.aimodels.fyi/models/huggingFace/qwen3-tts-12hz-1.7b-customvoice-qwen?utm_source=hackernoon&utm_medium=referral] maintained by Qwen [https://www.aimodels.fyi/creators/huggingFace/Qwen?utm_source=hackernoon&utm_medium=referral]. If you like these kinds of analysis, join AIModels.fyi [https://www.aimodels.fyi/?utm_source=hackernoon&utm_medium=referral] or follow us on Twitter [https://x.com/aimodelsfyi].


MODEL OVERVIEW

Qwen3-TTS-12Hz-1.7B-CustomVoice is a text-to-speech model created by Qwen [https://aimodels.fyi/creators/huggingFace/Qwen?utm_source=hackernoon&utm_medium=referral] that converts written text into natural-sounding speech across 10 major languages. The model supports instruction-based voice control, allowing users to select from 9 premium voice timbres that cover various combinations of gender, age, language, and dialect. This model represents a significant advancement in speech synthesis technology compared to earlier approaches, utilizing a discrete multi-codebook language model architecture that eliminates cascading errors found in traditional systems.

The architecture employs a Dual-Track hybrid streaming generation system, enabling both streaming and non-streaming audio production from a single model. The self-developed Qwen3-TTS-Tokenizer-12Hz powers the system, achieving efficient acoustic compression while maintaining high-fidelity speech reconstruction through a lightweight non-DiT architecture.


MODEL INPUTS AND OUTPUTS


INPUTS

 * Text content: Written text in any of the 10 supported languages (Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian)
 * Language specification: The target language for synthesis
 * Speaker selection: Choice from 9 predefined premium voice timbres
 * Optional instructions: Natural language directives for controlling tone, emotion, speaking rate, and prosody


OUTPUTS

 * Audio waveforms: High-fidelity speech audio generated in real-time or batch mode
 * Streaming capability: Immediate audio output after single character input, with end-to-end synthesis latency as low as 97ms


CAPABILITIES

The model understands context within text and applies that understanding to generate emotionally appropriate speech. It preserves paralinguistic information and acoustic environmental features through powerful speech representation. Users can instruct the model to adjust timbre and emotional expression based on text semantics, enabling the synthesis of speech that matches the intended tone of written content.

Streaming generation produces the first audio packet immediately after receiving minimal input, making this suitable for real-time interactive applications. The model handles noisy or imperfect input text with marked robustness, recovering gracefully from typos and formatting irregularities.


WHAT CAN I USE IT FOR?

Create interactive voice assistants that respond to users with natural speech in their preferred language. Build audiobook production systems that generate multiple voice variations from manuscripts. Develop multilingual customer service platforms where text responses are converted to speech with controlled emotional tone. Generate personalized audio messages where businesses customize voice characteristics to match brand identity. Implement real-time translation systems that pair translation with synthetic speech output in the user's native language. Create accessibility tools that convert written content to speech for individuals with visual impairments.


THINGS TO TRY

Experiment with instruction control by providing natural language descriptions of desired voice characteristics alongside your text. Test the streaming generation capability with very short text inputs to observe the rapid response latency. Compare outputs across different speaker timbres to understand the voice variety available within the model. Feed the model emotionally charged text and observe whether the synthesis reflects appropriate prosodic changes. Try multilingual inputs that mix languages within a single prompt to see how the model handles code-switching scenarios. Use the rapid voice cloning capability from the base model variant to understand the foundation this custom voice model builds upon.

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Original post: Read on AIModels.fyi [https://www.aimodels.fyi/models/huggingFace/qwen3-tts-12hz-1.7b-customvoice-qwen?utm_source=hackernoon&utm_medium=referral]

10 Languages, 9 Premium Voices: Meet Qwen3-TTS CustomVoice

Beldex will implement verifiable random functions in its consensus to enhance unpredictability and randomness in validator and block leader selection. 

Introducing Provable Randomness in Beldex Consensus with Verifiable Random Functions

As AI makes authoritative text cheap, verification becomes the bottleneck. A police report failure shows why institutions are adding friction.

Fluent AI Output Is Straining Human Verification Systems

Conventional Voice AI is binary with regards to interruptions. Language taxonomy could fix that.

Stop Muting Your Users: Building a Pragmatic Interruption State Machine for Voice AI

If life is a system, then Happiness is the output. 

Happiness = Variables - Frictions: The Source Code

Learn a practical AI-assisted workflow for line edits, character voice, and consistency—without ghostwriting. You stay the author; AI polishes.

Using AI for Line Edits, Voice, and Consistency (Without Cheating)

Learn how a Fortune 500 IT Director bridges the "Trust Gap" in Voice AI with a modular "Buy Fast, Build Safe" architecture designed for regulated industries.

Beyond the Hype: Why a “Trust Gap” Forced My Custom Voice AI Architecture

Learn how to write in your own voice, build confidence in your style, and create work that sounds unmistakably you.

How to Find Your “Voice” and Ideal Writing Workflow

Most AI interview platforms are glorified chatbots with better questions.

Why Voice Over Chat for AI Interviews (And Why It Almost Backfired)

Many users have lost faith in the integrity of the information they see. 

Establishing Verifiable Truth in a Post-Trust World

Crypto contract verification is the definitive proof of identity in the DeFi ecosystem. However, the process is often misunderstood.

The “Deterministic Black Box” That Keeps Failing Your Etherscan Verifications

Verifiability is becoming essential for crypto, AI, and cloud apps. Learn why proving how systems run will shape the next gen of secure infrastructure.

Why Verifiability Matters for Security: 12 Key Cloud Application Use Cases

Software Development

IT Services

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Vori is the only modern B2B operating system for the grocery industry (think Instacart or DoorDash but for grocery retailers and their wholesale suppliers)

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Vori's stories on HackerNoon

77 Stories To Learn About Decentralized Web

An Introduction to Skynet's Homescreen for Managing DeFi and DWeb Apps

Web Monetization In Crypto: The Skynet Way

Monero Vs. ZCash: The Definitive Comparision

Towards an Analytical Discipline of Forkonomy (Summer 2018)

Coinbase Valued At $8 Billion And Ripple’s XRapid Goes Into Commercial Production

Ripple’s XRapid Goes Into Commercial Production And Ethereum Enterprise Alliance Partners With…

Crypto Review — Siacoin (SC)

The Sound of Data: Why Your Voice is the Hardest Problem in AI

Voice AI: Here's What You Need to Know About It

Design a Voice With Words: Inside fal-ai’s qwen-3-tts/voice-design/1.7b

LuxTTS: Lightweight Voice Cloning That Fits in 1GB VRAM

10 Languages, 9 Premium Voices: Meet Qwen3-TTS CustomVoice

Introducing Provable Randomness in Beldex Consensus with Verifiable Random Functions

Fluent AI Output Is Straining Human Verification Systems

Stop Muting Your Users: Building a Pragmatic Interruption State Machine for Voice AI

Happiness = Variables - Frictions: The Source Code

Using AI for Line Edits, Voice, and Consistency (Without Cheating)

Beyond the Hype: Why a “Trust Gap” Forced My Custom Voice AI Architecture

How to Find Your “Voice” and Ideal Writing Workflow

Why Voice Over Chat for AI Interviews (And Why It Almost Backfired)

Establishing Verifiable Truth in a Post-Trust World

The “Deterministic Black Box” That Keeps Failing Your Etherscan Verifications

Why Verifiability Matters for Security: 12 Key Cloud Application Use Cases

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