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@hackernoon
hackernoon. com > how-to-build-a-real-time-voice-agent-with-pipecat

How to Build a Real-Time Voice Agent with Pipecat | Hacker Noon

20+ hour, 53+ min ago  (137+ words) This tutorial uses Pipecat " Daily. co's open-source Voice AI framework " to build a real-time voice agent that listens, thinks, and speaks. We'll use Assembly AI's Universal-3 Pro Streaming model for speech-to-text, GPT-4o for the language model, and Cartesia Sonic for…...

@hackernoon
hackernoon. com > tradition-is-telepathy-how-culture-enables-collective-intelligence

Tradition Is Telepathy: How Culture Enables Collective Intelligence | Hacker Noon

1+ day, 29+ min ago  (471+ words) This article argues that modern political and economic systems have weakened humanity's natural capacity for collective intelligence by fragmenting social cohesion and over-prioritizing individual competition. Drawing on neuroscience and cultural theory, it reframes tradition not as nostalgia but as a…...

@hackernoon
hackernoon. com > burmese-coder-4b-a-burmese-coding-llm-for-low-resource-language-ai

Burmese-Coder-4 B: A Burmese Coding LLM for Low-Resource Language AI | Hacker Noon

1+ day, 35+ min ago  (64+ words) I built Burmese-Coder-4 B, a Burmese coding LLM for low-resource language AI. This article covers the motivation, data creation, two-stage fine-tuning pipeline, evaluation approach, and lessons from building a practical Burmese coding model with limited compute. Myanmar AI researcher focused…...

@hackernoon
hackernoon. com > how-frontier-labs-use-fp8-to-train-faster-and-spend-less

How Frontier Labs Use FP8 to Train Faster and Spend Less | Hacker Noon

23+ hour, 58+ min ago  (1114+ words) A practical look at FP8 in LLM pretraining: how it works, where to apply it, what to watch out for, and what speedups you can realistically expect " with real numbers for Mo E model....

@hackernoon
hackernoon. com > designing-a-real-time-ai-voice-agent-with-rag-sip-integration-and-compliance-guardrails

Designing a Real-Time AI Voice Agent With RAG, SIP Integration, and Compliance Guardrails | Hacker Noon

2+ day, 7+ hour ago  (138+ words) Customer service automation has long promised faster support and lower costs. Until recently, most automated systems delivered the opposite: deep menu trees, limited speech recognition, and hardcoded logic. This is changing with the rise of LLM-powered AI voice agents. Designing…...

@hackernoon
hackernoon. com > scaling-dependency-graphs-for-real-time-computation-in-finance-and-beyond

Scaling Dependency Graphs for Real-Time Computation in Finance and Beyond | Hacker Noon

2+ day, 11+ hour ago  (979+ words) Real-time data processing is both the backbone and bottleneck of innovation. As systems grow more complex, conventional approaches to computation start to show their limits. In this article I explore a powerful alternative: using dependency graphs to model and execute…...

@hackernoon
hackernoon. com > how-to-evaluate-an-ai-persona-beyond-benchmarks-and-vibes

How to Evaluate an AI Persona: Beyond Benchmarks and Vibes

2+ day, 14+ hour ago  (46+ words) Hacker Noon How I Built a Persistent AI Persona That Passed Cognitive Testing (And What Broke Along the Way) AI persona on Claude. 29 voice rules, externalized memory, 413/430 cognitive score. Writing about AI from the inside....

@hackernoon
hackernoon. com > debunking-classical-pseudo-paradoxes-of-logic

Debunking Classical Pseudo-Paradoxes of Logic | Hacker Noon

3+ day, 7+ hour ago  (1026+ words) You know, many textbooks on mathematical logic look more like textbooks on scholasticism and metaphysics. On one hand, they promise to show you the strictest foundation of thought " and on the other, they tell you: "Well, but if things get…...

@hackernoon
hackernoon. com > two-training-paths-one-smarter-ai-strategy

Two Training Paths, One Smarter AI Strategy

4+ day, 13+ hour ago  (855+ words) Hacker Noon The two paths to better models Training large language models involves a fundamental choice between two different sources of feedback, each with its own strengths and weaknesses The first approach is on-policy distillation (OPD) Here, a larger teacher…...

@hackernoon
hackernoon. com > fine-tuning-vs-prompt-engineering

Fine-Tuning vs Prompt Engineering

4+ day, 4+ hour ago  (168+ words) Hacker Noon < 100 M inferences/month: Prompt Engineering " Fixed cost infrastructure dominates 100 M - 1 B inferences/month: Hybrid " Fine-tuning cost per inference becomes significant 1 B inferences/month: Fine-Tuning " Unit economics favour optimised model Frequent changes needed: Prompt Engineering " Update latency matters more…...