hello there, I'm a new member of this community, a VI developer learning NVGT currently. I had this question alot in my mind, that which free AI is best? I'm confused between gemini, chatgpt, deepseek and claude. Claude has limits, so does gemini and chatgpt. Deepseek I have never tried, but I'm looking for a free alternative, or an AI which is at least reliable, and at least doesn't spoils code l
Why this list is different The "best" email API depends entirely on what you're building. A side project optimizing for the free tier needs different things than a Series B SaaS sending two million transactional emails a month. This post grades eight providers against the criteria that actually move the needle in production, and tells you which one to pick for which use case. Most roundups in th
Revolutionize Mistral 2 vs RAG Comparisons: What Fails and How to Fix It Comparing Mistral 2, the widely adopted open-source large language model, to Retrieval-Augmented Generation (RAG) frameworks has become a common but deeply flawed practice in AI evaluation circles. This mismatch stems from a fundamental misunderstanding of what each tool is, how they interact, and what metrics actually matt
If you want to Automate GitHub PRs, the real goal is not just adding another bot comment to a pull request. The goal is to give reviewers the context they usually have to gather manually: who owns the service, whether it is deployed, whether basic repository standards are in place, and whether the change looks safe to merge. A useful AI pull request workflow can do exactly that. When a PR opens, i
Well, I have been on GitHub since 2019, even before the lockdown. Back then, I did not properly use GitHub. I used to just make projects, upload the code, and share it with friends. But I never really understood the point of GitHub. I think I missed my tutorials on GitHub. But now, I’ll share some key ways to actually make the best out of it. Your GitHub profile is not just a bio page. It is your
Comparison: Haystack 2.0 vs. RAGatouille 0.3 for Building High-Accuracy RAG Pipelines for Developer Docs Retrieval-Augmented Generation (RAG) has become the standard for building LLM-powered tools that answer questions using private or domain-specific data. For developer documentation (dev docs) — which includes technical jargon, versioned APIs, code snippets, and structured reference material —
A collective list of free APIs