18-Year-Old Pharmacy Student from Prayagraj Builds Browser Engine from Scratch

·5 views

Anirudh Mishra, an 18-year-old D.Pharm student from Prayagraj, has developed Shred Browser, a basic browser engine built entirely by himself. The project was driven primarily by his curiosity to understand how modern browsers work rather than to create a commercial product. Currently, the engine performs a simple task of stripping HTML tags from the example.org website, with more advanced versions planned for future release on GitHub. Mishra cites Elon Musk's first-principles thinking and perseverance as his key inspiration for pursuing technology despite being outside a traditional computer science field.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Generative vs Agentic AI: How to Choose the Right Tool in 2026

As of September 2026, generative AI remains the preferred choice for content creation tasks such as drafting, summarisation, and code snippets, due to its lower cost and ease of review. Agentic AI, which is built on top of generative models, is better suited for multi-step, goal-directed workflows like bug fixing or data reconciliation, where results can be automatically verified. Despite growing interest, adoption of agentic systems remains limited — only 23% of organisations have scaled one, while 39% are still experimenting. Gartner warns that over 40% of agentic AI projects could be cancelled by end of 2027, citing rising costs, unclear business value, and insufficient risk controls. Experts recommend a default-generative approach, escalating to agentic only for well-defined, verifiable workflows where a machine can reliably grade its own output.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Breeze TTS 2 Tops Open-Weight TTS Leaderboard but Bars Commercial Use

Breeze TTS 2, developed by BreezeBlue/RESONIA INC., reached the top spot among open-weight text-to-speech models on the Artificial Analysis leaderboard with an Elo score of 1215, surpassing ElevenLabs' Eleven v3 at 1175 as of September 11, 2026. Its hosted price of $34 per million characters is significantly cheaper than ElevenLabs' $100 for the same volume. However, while the model's inference code is licensed under Apache 2.0, the weights themselves are restricted to research and non-commercial use only, with commercial deployment requiring a separate written agreement from BreezeBlue. This makes Breeze TTS 2 a strong choice for researchers, hobbyists, and non-commercial builders with compatible GPU hardware, but unsuitable for businesses shipping paid products without prior licensing negotiations. ElevenLabs remains the recommended option for commercial use cases, given its unambiguous licensing, broader language support, and mature platform infrastructure.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

DeepSeek V4.1-Flash Matches Top Closed Models on Coding at a Fraction of the Cost

DeepSeek released V4.1-Flash on 10 September 2026 with MIT-licensed weights, positioning it as a cost-efficient open-source model for coding agents. On the DeepSWE v1.1 benchmark, it scores 74.2, nearly matching Claude Opus 5 (74.0) and GPT-5.6 Sol (73.0), according to DeepSeek's own release figures. Its off-peak cached-input rate of $0.003 per million tokens makes it dramatically cheaper than rivals, with one worked example showing $0.15 versus $25 for Claude Opus 5 on a 100-request agent loop. However, the model significantly trails on hard reasoning tasks, scoring 36.8 on Humanity's Last Exam compared to Claude Opus 5's 56.3, making it less suitable for complex single-answer problems. From 14 September 2026, all V4-Pro API requests are automatically rerouted to V4.1-Flash and billed at the lower Flash rate.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Jev markets itself as a frontier AI model but is essentially a JSON classifier

Jev, a newly launched AI startup founded by ex-OpenAI researchers, claims to offer hallucination-free outputs and frontier-model capabilities. In practice, the product functions as a constrained JSON classifier that picks from predefined options, scores against criteria, or returns true/false judgments. Its 'no hallucinations' claim relies on constrained decoding — a standard technique already widely used by OpenAI, Anthropic, and open-source tools — which simply prevents the model from emitting tokens outside a fixed set. Within hours of Jev's launch, a developer independently replicated the core functionality using a small open-source model on Hugging Face. The product's real value lies in cheaper, faster classification and routing at scale, but that cost-efficiency pitch has been packaged as a more ambitious frontier-AI narrative.

0 comments Read more at DEV Community