DEV Community: Ava Nichols

Has AI made programming less satisfying for you?

Ava Nichols — Thu, 06 Nov 2025 14:55:34 +0000

Hey, just curious if this is a sentiment out there?

Where does vibe coding most typically fall short for you?

Ava Nichols — Fri, 03 Oct 2025 13:43:59 +0000

Gemini 2.5 Flash Images launched https://deepmind.google/models/gemini/image/

Ava Nichols — Tue, 26 Aug 2025 17:13:35 +0000

Gemini 3.1 Flash Image – Nano Banana 2 — Google DeepMind

Pro-level image generation and editing – with the speed you expect from Flash.

deepmind.google

GPT 5

Ava Nichols — Thu, 07 Aug 2025 18:48:30 +0000

Ali Farhat

Aug 7 '25

GPT-5 Just Dropped. Here's Why Your Automation Stack Is Already Outdated

#gpt5 #automation #openai #aiagents

Comments 8

4 min read

Abstractions

Ava Nichols — Tue, 01 Apr 2025 14:32:21 +0000

Dumb

Ava Nichols — Tue, 01 Apr 2025 14:10:18 +0000

Deepseek R1

Ava Nichols — Mon, 20 Jan 2025 15:55:10 +0000

Deepseek's R1 Reasoning model is live and open source

deepseek-ai / DeepSeek-R1

DeepSeek-R1

Paper Link👁️

1. Introduction

We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning. With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors. However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models.

NOTE: Before running DeepSeek-R1 series models locally, we kindly recommend reviewing the Usage Recommendation section.

2. Model Summary

Post-Training: Large-Scale Reinforcement Learning on…

View on GitHub