📰 Hackernoon

When it came to using Large Language Models (LLMs), it used to be all about prompt engineering.

AI inference costs are going up, but adopting the habits of LLM Whisperers can deliver cost savings and other benefits.

How a production voice AI stack combines fine-tuned speech recognition, translation, caching, and streaming TTS for African languages.

dots.llm1.inst is an instruction-tuned mixture-of-experts language model built by dots-studio that activates 14 billion parameters out of a total of 142 billion

The release of Inkling marks the end of the "static model" era. It is a foundation meant for "Tinkering,"

seedream-5-pro is ByteDance's flagship text-to-image and image-to-image generation model capable of producing sharp images at 1K and 2K resolutions.

Qwythos-9B-v2 uses FTPO fine-tuning to eliminate looping under greedy decoding without reducing its core reasoning capabilities.

A cascading voice agent chains speech-to-text, an LLM, and text-to-speech — and every layer downstream can only react to the transcript it's handed. So your rea

Part 2 expands the AI dream analysis engine by integrating GPT with Retrieval-Augmented Generation, embeddings, vector search, and structured prompts. The artic

This article explains how an AI-powered travel application improved place photo accuracy by combining a fast online resolver with an offline verification pipeli

Google DeepMind postponed Gemini 3.5 Pro to July 17, 2026 after abandoning its original foundation in favor of a longer pre-training cycle. The move reflects gr

Quasar (Subnet 24), Gradients (Subnet 56), and Subnet 3 have joined forces on Bittensor to train long-context AI models. The collaboration pairs Quasar’s long-c

INTELLECT-3.1 is a 106B open-source MoE reasoning model from PrimeIntellect built for math, coding, software engineering, and agents.

The article outlines the first stage of building an AI-powered dream analysis system. Rather than relying on symbolic dream dictionaries, it proposes a context-

The result is that a casual, conversational request produces the kind of output that would normally require a carefully engineered prompt. Less setup, less fric

This interview profiles Visual MCP Gateway, an open source project built for the Proof of Usefulness Hackathon. The tool uses Anthropic's Model Context Protocol

OpenAI and Anthropic are bankrupting the world with the most important resource: intelligence. Here are 5 steps to reverse that balance and give the power to ev

Defaulting all developer queries to top-tier LLMs is an expensive engineering failure; dynamic routing at the gateway layer cuts costs by 50%.

AI memory should be product state: session notes, approved handoffs, retrieval evidence, and visible user control instead of hidden prompt context.

There is a well-known trick for climbing ASR leaderboards: fine-tune on data that closely resembles a testset, submit, watch your rank improve. We did exactly t

GLM-4.7-Flash should fly on a small box: 30B total, ~3B active. On a Radeon 780M mini PC it crawled and fell apart past short context. The cause was not the wei

Enterprise teams should choose between RAG, fine-tuning, and re-training based on what needs to change in the AI system. RAG improves access to current enterpri

The integration of Large Language Models (LLMs) into enterprise database environments has fundamentally altered how users interact with structured data. This pa

Chaining multiple LLM calls, vector database lookups, and API tools creates a severe performance bottleneck, dragging response times from seconds to minutes. Ev