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
What if your Kubernetes cluster simply refused to run unsigned images? I spent some time experimenting with enforcing image provenance in a small Kubernetes setup using MicroK8s. The idea was simple: Only container images with valid cryptographic signatures are allowed to run in the cluster. For this I used: GitLab CI/CD (build + signing pipeline) Cosign / Sigstore (image signing) Kyverno (admissi
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
Most teams I have worked with have one auth test in their suite. It looks like this: test('valid token verifies', () => { const token = signSync({ sub: 'user-1', aud: 'api://backend' }, secret); const result = verify(token, options); expect(result.valid).toBe(true); }); That test is fine. It is also a smoke test, not a regression suite. It catches the case where verification is completely b
The on-call alert at 02:14 said auth_5xx_rate spiked from 0.01 to 31.4. Not a deploy window. Not a traffic spike. Just thirty-one percent of authenticated requests failing for ~four minutes, then back to baseline. The cause was a JWKS rotation on the issuer side. New keys came in. Old keys went out. Caches in our service didn't refresh fast enough. Tokens signed with the new key were rejected beca
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 —